{"id":1137757,"date":"2025-01-08T21:52:15","date_gmt":"2025-01-08T13:52:15","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1137757.html"},"modified":"2025-01-08T21:52:17","modified_gmt":"2025-01-08T13:52:17","slug":"python%e5%a6%82%e4%bd%95%e8%ae%a9%e4%b8%80%e4%b8%aa%e7%9f%a9%e9%98%b5%e6%88%90%e4%b8%80%e8%a1%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1137757.html","title":{"rendered":"python\u5982\u4f55\u8ba9\u4e00\u4e2a\u77e9\u9635\u6210\u4e00\u884c"},"content":{"rendered":"

\"python\u5982\u4f55\u8ba9\u4e00\u4e2a\u77e9\u9635\u6210\u4e00\u884c\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5c06\u4e00\u4e2a\u77e9\u9635\u8f6c\u6362\u4e3a\u4e00\u884c\uff0c\u4f8b\u5982\u4f7f\u7528NumPy\u7684ravel()\u3001flatten()\u65b9\u6cd5\u3001\u5217\u8868\u63a8\u5bfc\u4ee5\u53caitertools.chAI<\/a>n()\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u7684\u65b9\u6cd5\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u7684ravel()\u65b9\u6cd5<\/h3>\n<\/p>\n

NumPy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u7684\u5f3a\u5927\u5e93\u3002\u901a\u8fc7\u4f7f\u7528ravel()\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5c06\u591a\u7ef4\u6570\u7ec4\u5c55\u5e73\u6210\u4e00\u7ef4\u6570\u7ec4\u3002ravel()\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u539f\u6570\u7ec4\u7684\u89c6\u56fe\uff0c\u8fd9\u610f\u5473\u7740\u4fee\u6539ravel()\u65b9\u6cd5\u8fd4\u56de\u7684\u6570\u7ec4\u4f1a\u5f71\u54cd\u539f\u6570\u7ec4\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.ravel()<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u7684flatten()\u65b9\u6cd5<\/h3>\n<\/p>\n
\u4e0eravel()\u65b9\u6cd5\u4e0d\u540c\uff0cflatten()\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u6570\u7ec4\u7684\u526f\u672c\uff0c\u56e0\u6b64\u5bf9\u5176\u8fdb\u884c\u4fee\u6539\u4e0d\u4f1a\u5f71\u54cd\u539f\u6570\u7ec4\u3002\u8fd9\u5728\u9700\u8981\u4fdd\u7559\u539f\u59cb\u6570\u636e\u7684\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.flatten()<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5217\u8868\u63a8\u5bfc<\/h3>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u662fPython\u4e2d\u4e00\u79cd\u7b80\u6d01\u7684\u8bed\u6cd5\u7ed3\u6784\uff0c\u7528\u4e8e\u521b\u5efa\u5217\u8868\u3002\u5728\u5904\u7406\u5c0f\u578b\u77e9\u9635\u65f6\uff0c\u5217\u8868\u63a8\u5bfc\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u76f4\u63a5\u7684\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = [element for row in matrix for element in row]<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528itertools.chain()<\/h3>\n<\/p>\n
itertools.chain()\u65b9\u6cd5\u53ef\u4ee5\u5c06\u591a\u4e2a\u8fed\u4ee3\u5668\u4e32\u8054\u8d77\u6765\uff0c\u5f62\u6210\u4e00\u4e2a\u5355\u4e00\u7684\u8fed\u4ee3\u5668\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5904\u7406\u66f4\u5927\u89c4\u6a21\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
import itertools<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = list(itertools.chain(*matrix))<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0NumPy\u7684ravel()\u65b9\u6cd5<\/h3>\n<\/p>\n
NumPy\u7684ravel()\u65b9\u6cd5\u4e0d\u4ec5\u53ef\u4ee5\u5c06\u77e9\u9635\u5c55\u5e73\u6210\u4e00\u884c\uff0c\u800c\u4e14\u5728\u6027\u80fd\u4e0a\u4e5f\u662f\u975e\u5e38\u9ad8\u6548\u7684\u3002\u56e0\u4e3aravel()\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u539f\u6570\u7ec4\u7684\u89c6\u56fe\uff08view\uff09\uff0c\u6240\u4ee5\u5b83\u51e0\u4e4e\u4e0d\u5360\u7528\u989d\u5916\u7684\u5185\u5b58\u7a7a\u95f4\u3002\u8fd9\u5bf9\u4e8e\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u573a\u666f\u5c24\u5176\u91cd\u8981\u3002\u6b64\u5916\uff0cravel()\u65b9\u6cd5\u8fd8\u5141\u8bb8\u6211\u4eec\u6307\u5b9a\u5c55\u5e73\u7684\u987a\u5e8f\uff0c\u4f8b\u5982\u6309\u884c\uff08\u9ed8\u8ba4\uff09\u6216\u8005\u6309\u5217\u3002\u8fd9\u4f7f\u5f97\u5b83\u5728\u5904\u7406\u9700\u8981\u7279\u5b9a\u987a\u5e8f\u7684\u6570\u636e\u65f6\u975e\u5e38\u7075\u6d3b\u3002<\/p>\n<\/p>\n
\u4f7f\u7528ravel()\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.ravel(order='F')  # \u6309\u5217\u5c55\u5e73<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0corder='F'\u8868\u793a\u6309\u5217\u5c55\u5e73\uff0c\u5373\u5148\u5c55\u5e73\u5217\u518d\u5c55\u5e73\u884c\u3002\u5982\u679c\u7701\u7565order\u53c2\u6570\uff0c\u9ed8\u8ba4\u6309\u884c\u5c55\u5e73\u3002<\/p>\n<\/p>\n
\u4e00\u3001NumPy\u7684ravel()\u65b9\u6cd5<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u7684\u5f3a\u5927\u5e93\u3002\u901a\u8fc7\u4f7f\u7528ravel()\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5c06\u591a\u7ef4\u6570\u7ec4\u5c55\u5e73\u6210\u4e00\u7ef4\u6570\u7ec4\u3002ravel()\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u539f\u6570\u7ec4\u7684\u89c6\u56fe\uff0c\u8fd9\u610f\u5473\u7740\u4fee\u6539ravel()\u65b9\u6cd5\u8fd4\u56de\u7684\u6570\u7ec4\u4f1a\u5f71\u54cd\u539f\u6570\u7ec4\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.ravel()<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86NumPy\u5e93\uff0c\u5e76\u521b\u5efa\u4e86\u4e00\u4e2a3×3\u7684\u77e9\u9635\u3002\u7136\u540e\uff0c\u901a\u8fc7\u8c03\u7528ravel()\u65b9\u6cd5\uff0c\u6211\u4eec\u5c06\u77e9\u9635\u5c55\u5e73\u4e3a\u4e00\u4e2a\u5305\u542b\u6240\u6709\u5143\u7d20\u7684\u4e00\u7ef4\u6570\u7ec4\u3002\u6700\u7ec8\uff0c\u6253\u5370\u51fa\u5c55\u5e73\u540e\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n
\u4e8c\u3001NumPy\u7684flatten()\u65b9\u6cd5<\/h3>\n<\/p>\n
\u4e0eravel()\u65b9\u6cd5\u4e0d\u540c\uff0cflatten()\u65b9\u6cd5\u8fd4\u56de\u7684\u662f\u6570\u7ec4\u7684\u526f\u672c\uff0c\u56e0\u6b64\u5bf9\u5176\u8fdb\u884c\u4fee\u6539\u4e0d\u4f1a\u5f71\u54cd\u539f\u6570\u7ec4\u3002\u8fd9\u5728\u9700\u8981\u4fdd\u7559\u539f\u59cb\u6570\u636e\u7684\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.flatten()<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e0e\u524d\u4e00\u4e2a\u4f8b\u5b50\u76f8\u540c\u7684\u77e9\u9635\u3002\u4e0d\u540c\u7684\u662f\uff0c\u8fd9\u6b21\u6211\u4eec\u4f7f\u7528flatten()\u65b9\u6cd5\u6765\u5c55\u5e73\u77e9\u9635\u3002\u6700\u7ec8\uff0c\u6253\u5370\u51fa\u5c55\u5e73\u540e\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u5217\u8868\u63a8\u5bfc<\/h3>\n<\/p>\n
\u5217\u8868\u63a8\u5bfc\u662fPython\u4e2d\u4e00\u79cd\u7b80\u6d01\u7684\u8bed\u6cd5\u7ed3\u6784\uff0c\u7528\u4e8e\u521b\u5efa\u5217\u8868\u3002\u5728\u5904\u7406\u5c0f\u578b\u77e9\u9635\u65f6\uff0c\u5217\u8868\u63a8\u5bfc\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u76f4\u63a5\u7684\u65b9\u5f0f\u3002<\/p>\n<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = [element for row in matrix for element in row]<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u4e2a\u5d4c\u5957\u7684\u5217\u8868\u63a8\u5bfc\u6765\u5c55\u5e73\u77e9\u9635\u3002\u9996\u5148\uff0c\u6211\u4eec\u904d\u5386\u6bcf\u4e00\u884c\uff0c\u7136\u540e\u904d\u5386\u6bcf\u4e00\u884c\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20\uff0c\u6700\u7ec8\u5c06\u6240\u6709\u5143\u7d20\u6dfb\u52a0\u5230\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u4e2d\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528itertools.chain()<\/h3>\n<\/p>\n
itertools.chain()\u65b9\u6cd5\u53ef\u4ee5\u5c06\u591a\u4e2a\u8fed\u4ee3\u5668\u4e32\u8054\u8d77\u6765\uff0c\u5f62\u6210\u4e00\u4e2a\u5355\u4e00\u7684\u8fed\u4ee3\u5668\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5904\u7406\u66f4\u5927\u89c4\u6a21\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
import itertools<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = list(itertools.chain(*matrix))<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86itertools\u5e93\uff0c\u7136\u540e\u4f7f\u7528chain()\u65b9\u6cd5\u5c06\u6240\u6709\u884c\u8fde\u63a5\u8d77\u6765\uff0c\u5f62\u6210\u4e00\u4e2a\u5355\u4e00\u7684\u8fed\u4ee3\u5668\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c06\u8fd9\u4e2a\u8fed\u4ee3\u5668\u8f6c\u6362\u4e3a\u4e00\u4e2a\u5217\u8868\u3002<\/p>\n<\/p>\n
\u4e94\u3001NumPy\u7684reshape()\u65b9\u6cd5<\/h3>\n<\/p>\n
\u867d\u7136reshape()\u65b9\u6cd5\u4e3b\u8981\u7528\u4e8e\u5c06\u6570\u7ec4\u91cd\u5851\u4e3a\u6307\u5b9a\u5f62\u72b6\uff0c\u4f46\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5c06\u77e9\u9635\u5c55\u5e73\u6210\u4e00\u884c\u3002\u901a\u8fc7\u6307\u5b9a\u65b0\u7684\u5f62\u72b6\u53c2\u6570\u4e3a-1\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u77e9\u9635\u5c55\u5e73\u4e3a\u4e00\u7ef4\u6570\u7ec4\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.reshape(-1)<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528reshape()\u65b9\u6cd5\u5c06\u77e9\u9635\u5c55\u5e73\u4e3a\u4e00\u7ef4\u6570\u7ec4\u3002-1\u8868\u793a\u81ea\u52a8\u8ba1\u7b97\u6570\u7ec4\u7684\u5927\u5c0f\uff0c\u4ee5\u9002\u5e94\u539f\u59cb\u6570\u636e\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
\u516d\u3001Pandas\u7684values.flatten()\u65b9\u6cd5<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u53e6\u4e00\u4e2a\u5f3a\u5927\u5e93\u3002\u867d\u7136Pandas\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u6846\uff0c\u4f46\u5b83\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u65b9\u4fbf\u7684\u65b9\u6cd5\u6765\u5904\u7406\u77e9\u9635\u3002\u901a\u8fc7\u4f7f\u7528values\u5c5e\u6027\u548cflatten()\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5c06\u77e9\u9635\u5c55\u5e73\u6210\u4e00\u884c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
matrix = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
flattened_matrix = matrix.values.flatten()<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e00\u4e2aPandas\u6570\u636e\u6846\uff0c\u7136\u540e\u901a\u8fc7values\u5c5e\u6027\u83b7\u53d6\u5176\u5e95\u5c42\u7684NumPy\u6570\u7ec4\u3002\u6700\u540e\uff0c\u4f7f\u7528flatten()\u65b9\u6cd5\u5c06\u6570\u7ec4\u5c55\u5e73\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528\u5185\u7f6e\u51fd\u6570sum()<\/h3>\n<\/p>\n
\u867d\u7136\u4e0d\u592a\u5e38\u89c1\uff0c\u4f46\u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528Python\u5185\u7f6e\u7684sum()\u51fd\u6570\u6765\u5c55\u5e73\u77e9\u9635\u3002\u901a\u8fc7\u6307\u5b9a\u8d77\u59cb\u503c\u4e3a\u4e00\u4e2a\u7a7a\u5217\u8868\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6240\u6709\u5143\u7d20\u5408\u5e76\u5230\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u4e2d\u3002<\/p>\n<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = sum(matrix, [])<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528sum()\u51fd\u6570\u6765\u5c55\u5e73\u77e9\u9635\u3002\u901a\u8fc7\u6307\u5b9a\u8d77\u59cb\u503c\u4e3a\u4e00\u4e2a\u7a7a\u5217\u8868\uff0csum()\u51fd\u6570\u5c06\u6bcf\u4e00\u884c\u7684\u5143\u7d20\u5408\u5e76\u5230\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u4e2d\u3002<\/p>\n<\/p>\n
\u516b\u3001\u4f7f\u7528reduce()\u548coperator.concat()<\/h3>\n<\/p>\n
reduce()\u51fd\u6570\u548coperator.concat()\u65b9\u6cd5\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528\u6765\u5c55\u5e73\u77e9\u9635\u3002reduce()\u51fd\u6570\u5e94\u7528\u4e00\u4e2a\u4e8c\u5143\u51fd\u6570\uff08\u4f8b\u5982operator.concat()\uff09\u4e8e\u5217\u8868\u7684\u5143\u7d20\uff0c\u76f4\u5230\u5217\u8868\u88ab\u7b80\u5316\u4e3a\u4e00\u4e2a\u5355\u4e00\u7684\u503c\u3002<\/p>\n<\/p>\n
from functools import reduce<\/p>\n
import operator<\/p>\n
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]<\/p>\n
flattened_matrix = reduce(operator.concat, matrix)<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86reduce()\u51fd\u6570\u548coperator.concat()\u65b9\u6cd5\u3002\u7136\u540e\uff0c\u901a\u8fc7\u5e94\u7528operator.concat()\u4e8e\u77e9\u9635\u7684\u6bcf\u4e00\u884c\uff0c\u6211\u4eec\u5c06\u77e9\u9635\u5c55\u5e73\u4e3a\u4e00\u884c\u3002<\/p>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528\u591a\u7ef4\u5217\u8868\u63a8\u5bfc<\/h3>\n<\/p>\n
\u591a\u7ef4\u5217\u8868\u63a8\u5bfc\u53ef\u4ee5\u7528\u4e8e\u5c55\u5e73\u66f4\u590d\u6742\u7684\u591a\u7ef4\u77e9\u9635\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5c55\u5e73\u9ad8\u7ef4\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
matrix = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]<\/p>\n
flattened_matrix = [element for sublist1 in matrix for sublist2 in sublist1 for element in sublist2]<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u4e2a\u4e09\u91cd\u5d4c\u5957\u7684\u5217\u8868\u63a8\u5bfc\u6765\u5c55\u5e73\u4e00\u4e2a\u4e09\u7ef4\u77e9\u9635\u3002\u9996\u5148\uff0c\u6211\u4eec\u904d\u5386\u6700\u5916\u5c42\u7684\u5217\u8868\uff0c\u7136\u540e\u904d\u5386\u6bcf\u4e00\u4e2a\u5b50\u5217\u8868\uff0c\u6700\u540e\u904d\u5386\u6bcf\u4e00\u4e2a\u5b50\u5217\u8868\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20\uff0c\u6700\u7ec8\u5c06\u6240\u6709\u5143\u7d20\u6dfb\u52a0\u5230\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u4e2d\u3002<\/p>\n<\/p>\n
\u5341\u3001\u4f7f\u7528NumPy\u7684concatenate()\u65b9\u6cd5<\/h3>\n<\/p>\n
NumPy\u7684concatenate()\u65b9\u6cd5\u53ef\u4ee5\u7528\u4e8e\u5c06\u591a\u4e2a\u6570\u7ec4\u8fde\u63a5\u8d77\u6765\uff0c\u5f62\u6210\u4e00\u4e2a\u5355\u4e00\u7684\u6570\u7ec4\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u9700\u8981\u5c06\u591a\u4e2a\u77e9\u9635\u5c55\u5e73\u5e76\u8fde\u63a5\u5728\u4e00\u8d77\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
matrix1 = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
matrix2 = np.array([[7, 8, 9], [10, 11, 12]])<\/p>\n
flattened_matrix = np.concatenate((matrix1.ravel(), matrix2.ravel()))<\/p>\n
print(flattened_matrix)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e86\u4e24\u4e2a\u77e9\u9635\uff0c\u7136\u540e\u901a\u8fc7ravel()\u65b9\u6cd5\u5c06\u5b83\u4eec\u5206\u522b\u5c55\u5e73\u3002\u6700\u540e\uff0c\u4f7f\u7528concatenate()\u65b9\u6cd5\u5c06\u5c55\u5e73\u540e\u7684\u6570\u7ec4\u8fde\u63a5\u5728\u4e00\u8d77\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u5c06\u77e9\u9635\u5c55\u5e73\u6210\u4e00\u884c\u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\u662f\u4e00\u4e2a\u5e38\u89c1\u4e14\u91cd\u8981\u7684\u4efb\u52a1\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff0c\u4eceNumPy\u548cPandas\u7b49\u5f3a\u5927\u7684\u5e93\u5230\u5185\u7f6e\u7684\u5217\u8868\u63a8\u5bfc\u548citertools\u5e93\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u9700\u6c42\u548c\u6570\u636e\u89c4\u6a21\u3002\u4f8b\u5982\uff0cNumPy\u7684ravel()\u548cflatten()\u65b9\u6cd5\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u975e\u5e38\u9ad8\u6548\uff0c\u800c\u5217\u8868\u63a8\u5bfc\u548citertools.chain()\u65b9\u6cd5\u5219\u9002\u7528\u4e8e\u8f83\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u7406\u89e3\u5176\u5de5\u4f5c\u539f\u7406\u548c\u9002\u7528\u573a\u666f\u90fd\u6709\u52a9\u4e8e\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5c06\u77e9\u9635\u8f6c\u6362\u4e3a\u4e00\u7ef4\u6570\u7ec4\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u8f7b\u677e\u5730\u5c06\u77e9\u9635\u8f6c\u6362\u4e3a\u4e00\u7ef4\u6570\u7ec4\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528numpy.ravel()<\/code>\u6216numpy.flatten()<\/code>\u51fd\u6570\u3002\u8fd9\u4e24\u4e2a\u51fd\u6570\u90fd\u80fd\u5c06\u591a\u7ef4\u6570\u7ec4\u8f6c\u6362\u4e3a\u4e00\u7ef4\u6570\u7ec4\uff0c\u4f46ravel()<\/code>\u8fd4\u56de\u7684\u662f\u89c6\u56fe\uff0c\u800cflatten()<\/code>\u8fd4\u56de\u7684\u662f\u526f\u672c\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import numpy as np\n\nmatrix = np.array([[1, 2, 3], [4, 5, 6]])\none_d_array = matrix.flatten()  # \u6216\u4f7f\u7528 matrix.ravel()\nprint(one_d_array)\n<\/code><\/pre>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u4e0d\u89c4\u5219\u77e9\u9635\u7684\u8f6c\u6362\uff1f<\/strong>
\u5bf9\u4e8e\u4e0d\u89c4\u5219\u77e9\u9635\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u6765\u5b9e\u73b0\u5c06\u5176\u8f6c\u6362\u4e3a\u4e00\u884c\u3002\u6bd4\u5982\uff0c\u5047\u8bbe\u60a8\u6709\u4e00\u4e2a\u4e0d\u89c4\u5219\u7684\u5d4c\u5957\u5217\u8868\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u5c06\u5176\u5c55\u5f00\u4e3a\u4e00\u7ef4\u5217\u8868\uff1a<\/p>\n
matrix = [[1, 2, 3], [4, 5], [6]]\none_d_list = [item for sublist in matrix for item in sublist]\nprint(one_d_list)\n<\/code><\/pre>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u5c06\u6240\u6709\u5b50\u5217\u8868\u7684\u5143\u7d20\u63d0\u53d6\u5230\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u4e2d\u3002<\/p>\n
\u4f7f\u7528Pandas\u5982\u4f55\u5c06\u77e9\u9635\u8f6c\u6362\u4e3a\u4e00\u884c\uff1f<\/strong>
\u5982\u679c\u60a8\u7684\u6570\u636e\u662f\u4ee5DataFrame\u7684\u5f62\u5f0f\u5b58\u5728\uff0cPandas\u63d0\u4f9b\u4e86values.flatten()<\/code>\u65b9\u6cd5\u3002\u9996\u5148\u786e\u4fdd\u60a8\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
import pandas as pd\n\ndf = pd.DataFrame([[1, 2, 3], [4, 5, 6]])\none_d_array = df.values.flatten()\nprint(one_d_array)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u6cd5\u80fd\u591f\u6709\u6548\u5904\u7406DataFrame\u4e2d\u7684\u6570\u636e\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u4e00\u7ef4\u6570\u7ec4\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5c06\u4e00\u4e2a\u77e9\u9635\u8f6c\u6362\u4e3a\u4e00\u884c\uff0c\u4f8b\u5982\u4f7f\u7528NumPy\u7684ravel()\u3001flatten()\u65b9\u6cd5\u3001 […]","protected":false},"author":3,"featured_media":1137763,"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\/1137757"}],"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=1137757"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1137757\/revisions"}],"predecessor-version":[{"id":1137764,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1137757\/revisions\/1137764"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1137763"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1137757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1137757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1137757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}