{"id":1151692,"date":"2025-01-13T17:16:40","date_gmt":"2025-01-13T09:16:40","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1151692.html"},"modified":"2025-01-13T17:16:43","modified_gmt":"2025-01-13T09:16:43","slug":"python%e5%a6%82%e4%bd%95%e6%8f%90%e5%8d%87%e5%88%86%e6%9e%90%e9%97%ae%e9%a2%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1151692.html","title":{"rendered":"python\u5982\u4f55\u63d0\u5347\u5206\u6790\u95ee\u9898"},"content":{"rendered":"

\"python\u5982\u4f55\u63d0\u5347\u5206\u6790\u95ee\u9898\"<\/p>\n

Python\u63d0\u5347\u5206\u6790\u95ee\u9898\u7684\u6838\u5fc3\u65b9\u6cd5\u6709\uff1a\u6570\u636e\u5904\u7406\u80fd\u529b\u5f3a\u3001\u4e30\u5bcc\u7684\u5e93\u652f\u6301\u3001\u53ef\u89c6\u5316\u5de5\u5177\u3001\u673a\u5668\u5b66\u4e60<\/a>\u4e0e\u6df1\u5ea6\u5b66\u4e60\u3001\u81ea\u52a8\u5316\u811a\u672c\u3001\u5f3a\u5927\u7684\u793e\u533a\u652f\u6301\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u5904\u7406\u80fd\u529b\u5f3a\u662fPython\u5728\u6570\u636e\u5206\u6790\u9886\u57df\u5e7f\u53d7\u6b22\u8fce\u7684\u5173\u952e\u56e0\u7d20\u3002Python\u63d0\u4f9b\u4e86Pandas\u3001Numpy\u7b49\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u5feb\u901f\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u5904\u7406\u548c\u5206\u6790\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u3002\u8fd9\u4e9b\u5e93\u80fd\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u529f\u80fd\uff0c\u4f7f\u6570\u636e\u5206\u6790\u53d8\u5f97\u66f4\u52a0\u4fbf\u6377\u548c\u9ad8\u6548\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5904\u7406\u80fd\u529b\u5f3a<\/h3>\n<\/p>\n

Python\u62e5\u6709\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u548cNumpy\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u65b9\u6cd5\u3002Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u5b83\u652f\u6301\u6570\u636e\u7684\u6e05\u6d17\u3001\u64cd\u7eb5\u548c\u805a\u5408\u3002Numpy\u662f\u4e00\u4e2a\u652f\u6301\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\u3002<\/p>\n<\/p>\n

1. Pandas\u5e93<\/h4>\n<\/p>\n

Pandas\u5e93\u662fPython\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86DataFrame\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002DataFrame\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u7684\u5bfc\u5165\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u901a\u8fc7Pandas\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5b8c\u6210\u6570\u636e\u7684\u7b5b\u9009\u3001\u6392\u5e8f\u3001\u5408\u5e76\u3001\u5206\u7ec4\u548c\u7edf\u8ba1\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

2. Numpy\u5e93<\/h4>\n<\/p>\n

Numpy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406\u3002Numpy\u7684\u6570\u7ec4\u5bf9\u8c61ndarray\u652f\u6301\u591a\u7ef4\u6570\u7ec4\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u5411\u91cf\u5316\u8fd0\u7b97\u3002Numpy\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\u5e93\uff0c\u5982\u7ebf\u6027\u4ee3\u6570\u3001\u5085\u91cc\u53f6\u53d8\u6362\u7b49\uff0c\u53ef\u4ee5\u6ee1\u8db3\u5404\u79cd\u6570\u503c\u8ba1\u7b97\u9700\u6c42\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4e30\u5bcc\u7684\u5e93\u652f\u6301<\/h3>\n<\/p>\n

Python\u62e5\u6709\u4e30\u5bcc\u7684\u7b2c\u4e09\u65b9\u5e93\uff0c\u8fd9\u4e9b\u5e93\u8986\u76d6\u4e86\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u3001\u6df1\u5ea6\u5b66\u4e60\u3001\u53ef\u89c6\u5316\u7b49\u5404\u4e2a\u9886\u57df\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u5b9e\u73b0\u5404\u79cd\u6570\u636e\u5206\u6790\u548c\u5efa\u6a21\u4efb\u52a1\u3002<\/p>\n<\/p>\n

1. Scikit-learn\u5e93<\/h4>\n<\/p>\n

Scikit-learn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u5de5\u5177\u3002Scikit-learn\u652f\u6301\u6570\u636e\u9884\u5904\u7406\u3001\u7279\u5f81\u9009\u62e9\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u6a21\u578b\u8bc4\u4f30\u548c\u6a21\u578b\u9009\u62e9\u7b49\u5168\u6d41\u7a0b\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002\u901a\u8fc7Scikit-learn\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u5404\u79cd\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u548c\u964d\u7ef4\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n

2. TensorFlow\u548cPyTorch\u5e93<\/h4>\n<\/p>\n

TensorFlow\u548cPyTorch\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u5e93\uff0c\u5b83\u4eec\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u795e\u7ecf\u7f51\u7edc\u6784\u5efa\u548c\u8bad\u7ec3\u5de5\u5177\u3002TensorFlow\u7531Google\u5f00\u53d1\uff0c\u652f\u6301\u5927\u89c4\u6a21\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0cPyTorch\u7531Facebook\u5f00\u53d1\uff0c\u5177\u6709\u52a8\u6001\u8ba1\u7b97\u56fe\u7684\u7279\u70b9\uff0c\u9002\u5408\u7814\u7a76\u548c\u5f00\u53d1\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u6784\u5efa\u548c\u8bad\u7ec3\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u89e3\u51b3\u590d\u6742\u7684\u56fe\u50cf\u3001\u8bed\u97f3\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\u95ee\u9898\u3002<\/p>\n<\/p>\n

\u4e09\u3001\u53ef\u89c6\u5316\u5de5\u5177<\/h3>\n<\/p>\n

Python\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c\u8fd9\u4e9b\u5de5\u5177\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u5206\u6790\u7ed3\u679c\u3002\u901a\u8fc7\u53ef\u89c6\u5316\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u3001\u8d8b\u52bf\u548c\u5173\u7cfb\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u51c6\u786e\u7684\u5206\u6790\u548c\u51b3\u7b56\u3002<\/p>\n<\/p>\n

1. Matplotlib\u5e93<\/h4>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u51fd\u6570\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u7b49\u3002Matplotlib\u652f\u6301\u81ea\u5b9a\u4e49\u56fe\u8868\u7684\u6837\u5f0f\u3001\u989c\u8272\u3001\u6807\u7b7e\u7b49\uff0c\u53ef\u4ee5\u6ee1\u8db3\u5404\u79cd\u6570\u636e\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/p>\n<\/p>\n

2. Seaborn\u5e93<\/h4>\n<\/p>\n

Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u7ed8\u56fe\u63a5\u53e3\u3002Seaborn\u652f\u6301\u591a\u79cd\u9ad8\u7ea7\u56fe\u8868\uff0c\u5982\u5206\u7c7b\u56fe\u3001\u56de\u5f52\u56fe\u3001\u70ed\u529b\u56fe\u7b49\uff0c\u9002\u5408\u8fdb\u884c\u590d\u6742\u6570\u636e\u7684\u53ef\u89c6\u5316\u5206\u6790\u3002\u901a\u8fc7Seaborn\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u63a2\u7d22\u6027\u5206\u6790\u548c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n

\u56db\u3001\u673a\u5668\u5b66\u4e60\u4e0e\u6df1\u5ea6\u5b66\u4e60<\/h3>\n<\/p>\n

Python\u5728\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u8868\u73b0\u51fa\u8272\uff0c\u62e5\u6709\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\u652f\u6301\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u5404\u79cd\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u4ece\u800c\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n

1. \u673a\u5668\u5b66\u4e60\u5e93<\/h4>\n<\/p>\n

\u9664\u4e86\u524d\u9762\u63d0\u5230\u7684Scikit-learn\u5e93\uff0cPython\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982XGBoost\u3001LightGBM\u3001CatBoost\u7b49\u3002\u8fd9\u4e9b\u5e93\u652f\u6301\u9ad8\u6548\u7684\u68af\u5ea6\u63d0\u5347\u7b97\u6cd5\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u548c\u9ad8\u7ef4\u7279\u5f81\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u8bad\u7ec3\u548c\u8c03\u4f18\u9ad8\u6027\u80fd\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002<\/p>\n<\/p>\n

2. \u6df1\u5ea6\u5b66\u4e60\u5e93<\/h4>\n<\/p>\n

\u9664\u4e86\u524d\u9762\u63d0\u5230\u7684TensorFlow\u548cPyTorch\u5e93\uff0cPython\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u6df1\u5ea6\u5b66\u4e60\u5e93\uff0c\u5982Keras\u3001MXNet\u3001Caffe\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u9ad8\u5c42\u6b21\u7684\u795e\u7ecf\u7f51\u7edc\u6784\u5efa\u63a5\u53e3\uff0c\u652f\u6301\u591a\u79cd\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\u3002\u901a\u8fc7\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u5404\u79cd\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002<\/p>\n<\/p>\n

\u4e94\u3001\u81ea\u52a8\u5316\u811a\u672c<\/h3>\n<\/p>\n

Python\u53ef\u4ee5\u7f16\u5199\u81ea\u52a8\u5316\u811a\u672c\uff0c\u81ea\u52a8\u5316\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4efb\u52a1\u3002\u901a\u8fc7\u7f16\u5199\u811a\u672c\uff0c\u7528\u6237\u53ef\u4ee5\u5b9e\u73b0\u6570\u636e\u7684\u5b9a\u65f6\u91c7\u96c6\u3001\u6e05\u6d17\u3001\u5904\u7406\u548c\u5206\u6790\uff0c\u4ece\u800c\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u6570\u636e\u5206\u6790\u7684\u53ca\u65f6\u6027\u3002<\/p>\n<\/p>\n

1. \u6570\u636e\u91c7\u96c6\u811a\u672c<\/h4>\n<\/p>\n

Python\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u91c7\u96c6\u5de5\u5177\uff0c\u5982Requests\u3001BeautifulSoup\u3001Scrapy\u7b49\uff0c\u8fd9\u4e9b\u5de5\u5177\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u7f51\u9875\u3001API\u548c\u6570\u636e\u5e93\u4e2d\u91c7\u96c6\u6570\u636e\u3002\u901a\u8fc7\u7f16\u5199\u6570\u636e\u91c7\u96c6\u811a\u672c\uff0c\u7528\u6237\u53ef\u4ee5\u5b9a\u65f6\u83b7\u53d6\u6700\u65b0\u7684\u6570\u636e\uff0c\u5b9e\u73b0\u6570\u636e\u7684\u5b9e\u65f6\u66f4\u65b0\u3002<\/p>\n<\/p>\n

2. \u6570\u636e\u5904\u7406\u811a\u672c<\/h4>\n<\/p>\n

\u901a\u8fc7\u4f7f\u7528Pandas\u3001Numpy\u7b49\u6570\u636e\u5904\u7406\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u7f16\u5199\u81ea\u52a8\u5316\u7684\u6570\u636e\u5904\u7406\u811a\u672c\uff0c\u5b9e\u73b0\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002\u901a\u8fc7\u7f16\u5199\u6570\u636e\u5904\u7406\u811a\u672c\uff0c\u7528\u6237\u53ef\u4ee5\u81ea\u52a8\u5316\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0c\u907f\u514d\u624b\u52a8\u64cd\u4f5c\u7684\u7e41\u7410\u548c\u9519\u8bef\u3002<\/p>\n<\/p>\n

\u516d\u3001\u5f3a\u5927\u7684\u793e\u533a\u652f\u6301<\/h3>\n<\/p>\n

Python\u62e5\u6709\u5e9e\u5927\u7684\u7528\u6237\u793e\u533a\u548c\u4e30\u5bcc\u7684\u5b66\u4e60\u8d44\u6e90\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u5404\u79cd\u95ee\u9898\u7684\u89e3\u51b3\u65b9\u6848\u548c\u5b66\u4e60\u8d44\u6599\u3002\u901a\u8fc7\u53c2\u4e0e\u793e\u533a\u8ba8\u8bba\u548c\u5b66\u4e60\uff0c\u7528\u6237\u53ef\u4ee5\u4e0d\u65ad\u63d0\u5347\u81ea\u5df1\u7684\u6570\u636e\u5206\u6790\u80fd\u529b\u548c\u6280\u80fd\u3002<\/p>\n<\/p>\n

1. \u793e\u533a\u8bba\u575b<\/h4>\n<\/p>\n

Python\u6709\u591a\u4e2a\u6d3b\u8dc3\u7684\u793e\u533a\u8bba\u575b\uff0c\u5982Stack Overflow\u3001Reddit\u3001Quora\u7b49\uff0c\u8fd9\u4e9b\u8bba\u575b\u4e0a\u6709\u5927\u91cf\u7684Python\u7528\u6237\u548c\u4e13\u5bb6\uff0c\u7528\u6237\u53ef\u4ee5\u5728\u8bba\u575b\u4e0a\u63d0\u95ee\u548c\u8ba8\u8bba\uff0c\u83b7\u53d6\u5404\u79cd\u95ee\u9898\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n

2. \u5b66\u4e60\u8d44\u6e90<\/h4>\n<\/p>\n

Python\u6709\u4e30\u5bcc\u7684\u5b66\u4e60\u8d44\u6e90\uff0c\u5982\u5b98\u65b9\u6587\u6863\u3001\u5728\u7ebf\u8bfe\u7a0b\u3001\u4e66\u7c4d\u3001\u535a\u5ba2\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u5b66\u4e60\u8fd9\u4e9b\u8d44\u6e90\uff0c\u7cfb\u7edf\u5730\u638c\u63e1Python\u7684\u57fa\u7840\u77e5\u8bc6\u548c\u9ad8\u7ea7\u6280\u80fd\uff0c\u4e0d\u65ad\u63d0\u5347\u81ea\u5df1\u7684\u6570\u636e\u5206\u6790\u80fd\u529b\u3002<\/p>\n<\/p>\n

\u4e03\u3001\u5b9e\u8df5\u6848\u4f8b<\/h3>\n<\/p>\n

\u901a\u8fc7\u5b9e\u9645\u6848\u4f8b\u7684\u5e94\u7528\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1Python\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5b9e\u9645\u6848\u4f8b\uff1a<\/p>\n<\/p>\n

1. \u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h4>\n<\/p>\n

\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\uff0c\u6570\u636e\u901a\u5e38\u662f\u6742\u4e71\u548c\u4e0d\u5b8c\u6574\u7684\uff0c\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u3002\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u6e05\u6d17\u548c\u8f6c\u6362\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7Pandas\u7684dropna()<\/code>\u51fd\u6570\u53bb\u9664\u7f3a\u5931\u503c\uff0c\u901a\u8fc7fillna()<\/code>\u51fd\u6570\u586b\u5145\u7f3a\u5931\u503c\uff0c\u901a\u8fc7replace()<\/code>\u51fd\u6570\u66ff\u6362\u5f02\u5e38\u503c\u7b49\u3002<\/p>\n<\/p>\n

2. \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n

\u901a\u8fc7\u4f7f\u7528Matplotlib\u548cSeaborn\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u5206\u6790\u7ed3\u679c\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7Matplotlib\u7684plot()<\/code>\u51fd\u6570\u751f\u6210\u6298\u7ebf\u56fe\uff0c\u901a\u8fc7bar()<\/code>\u51fd\u6570\u751f\u6210\u67f1\u72b6\u56fe\uff0c\u901a\u8fc7scatter()<\/code>\u51fd\u6570\u751f\u6210\u6563\u70b9\u56fe\u7b49\u3002\u901a\u8fc7Seaborn\u7684heatmap()<\/code>\u51fd\u6570\u751f\u6210\u70ed\u529b\u56fe\uff0c\u901a\u8fc7pAI<\/a>rplot()<\/code>\u51fd\u6570\u751f\u6210\u6210\u5bf9\u5173\u7cfb\u56fe\u7b49\u3002<\/p>\n<\/p>\n

3. \u673a\u5668\u5b66\u4e60\u5efa\u6a21<\/h4>\n<\/p>\n

\u901a\u8fc7\u4f7f\u7528Scikit-learn\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u5efa\u6a21\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7Scikit-learn\u7684train_test_split()<\/code>\u51fd\u6570\u8fdb\u884c\u6570\u636e\u96c6\u7684\u5212\u5206\uff0c\u901a\u8fc7LinearRegression()<\/code>\u7c7b\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u901a\u8fc7accuracy_score()<\/code>\u51fd\u6570\u8fdb\u884c\u6a21\u578b\u7684\u8bc4\u4f30\u7b49\u3002<\/p>\n<\/p>\n

4. \u6df1\u5ea6\u5b66\u4e60\u5efa\u6a21<\/h4>\n<\/p>\n

\u901a\u8fc7\u4f7f\u7528TensorFlow\u548cKeras\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u5efa\u6a21\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7TensorFlow\u7684Sequential<\/code>\u7c7b\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff0c\u901a\u8fc7compile()<\/code>\u51fd\u6570\u7f16\u8bd1\u6a21\u578b\uff0c\u901a\u8fc7fit()<\/code>\u51fd\u6570\u8bad\u7ec3\u6a21\u578b\uff0c\u901a\u8fc7evaluate()<\/code>\u51fd\u6570\u8bc4\u4f30\u6a21\u578b\u7b49\u3002<\/p>\n<\/p>\n

\u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n

\u7efc\u4e0a\u6240\u8ff0\uff0cPython\u901a\u8fc7\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3001\u4e30\u5bcc\u7684\u5e93\u652f\u6301\u3001\u4f18\u79c0\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3001\u5148\u8fdb\u7684\u673a\u5668\u5b66\u4e60\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u3001\u4fbf\u6377\u7684\u81ea\u52a8\u5316\u811a\u672c\u548c\u5f3a\u5927\u7684\u793e\u533a\u652f\u6301\uff0c\u5927\u5927\u63d0\u5347\u4e86\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002\u901a\u8fc7\u5b9e\u9645\u6848\u4f8b\u7684\u5e94\u7528\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u638c\u63e1Python\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528\uff0c\u4e0d\u65ad\u63d0\u5347\u81ea\u5df1\u7684\u6570\u636e\u5206\u6790\u80fd\u529b\u548c\u6280\u80fd\u3002<\/p>\n<\/p>\n

\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n

\u5982\u4f55\u4f7f\u7528Python\u6765\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u6548\u7387\uff1f<\/strong>
Python\u62e5\u6709\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u6781\u5927\u5730\u63d0\u5347\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u3002\u4f8b\u5982\uff0cPandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u4f7f\u5f97\u6570\u636e\u6e05\u6d17\u548c\u8f6c\u6362\u53d8\u5f97\u66f4\u52a0\u5feb\u6377\u3002NumPy\u5219\u53ef\u4ee5\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u53ef\u4ee5\u7528\u6765\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u4ece\u800c\u5e2e\u52a9\u5206\u6790\u8005\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u8d8b\u52bf\u3002<\/p>\n

\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cPython\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5e93\u548c\u5de5\u5177\uff1f<\/strong>
\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u6709\u51e0\u4e2aPython\u5e93\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\uff0c\u5305\u62ecPandas\u3001NumPy\u3001Matplotlib\u3001Seaborn\u548cSciPy\u3002Pandas\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\uff0cNumPy\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib\u548cSeaborn\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u800cSciPy\u5219\u63d0\u4f9b\u4e86\u591a\u79cd\u79d1\u5b66\u8ba1\u7b97\u529f\u80fd\uff0c\u8fd9\u4e9b\u5de5\u5177\u7684\u7ed3\u5408\u80fd\u591f\u4e3a\u6570\u636e\u5206\u6790\u63d0\u4f9b\u5168\u9762\u7684\u652f\u6301\u3002<\/p>\n

\u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1f<\/strong>
\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u73af\u8282\u3002Python\u7684Pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u5305\u62ec\u586b\u5145\u7f3a\u5931\u503c\uff08\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\uff09\u3001\u5220\u9664\u7f3a\u5931\u6570\u636e\u884c\u6216\u5217\u3001\u4ee5\u53ca\u4f7f\u7528\u63d2\u503c\u6cd5\u7b49\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u6e05\u7406\u6570\u636e\u96c6\uff0c\u786e\u4fdd\u5206\u6790\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u63d0\u5347\u5206\u6790\u95ee\u9898\u7684\u6838\u5fc3\u65b9\u6cd5\u6709\uff1a\u6570\u636e\u5904\u7406\u80fd\u529b\u5f3a\u3001\u4e30\u5bcc\u7684\u5e93\u652f\u6301\u3001\u53ef\u89c6\u5316\u5de5\u5177\u3001\u673a\u5668\u5b66\u4e60\u4e0e\u6df1\u5ea6\u5b66\u4e60\u3001\u81ea\u52a8\u5316\u811a\u672c […]","protected":false},"author":3,"featured_media":1151698,"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\/1151692"}],"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=1151692"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1151692\/revisions"}],"predecessor-version":[{"id":1151700,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1151692\/revisions\/1151700"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1151698"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1151692"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1151692"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1151692"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}