{"id":1118562,"date":"2025-01-08T18:36:31","date_gmt":"2025-01-08T10:36:31","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1118562.html"},"modified":"2025-01-08T18:36:33","modified_gmt":"2025-01-08T10:36:33","slug":"%e5%a6%82%e4%bd%95%e5%b0%86%e4%b8%89%e7%bb%b4%e7%9f%a9%e9%98%b5%e5%ad%98%e5%82%a8python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1118562.html","title":{"rendered":"\u5982\u4f55\u5c06\u4e09\u7ef4\u77e9\u9635\u5b58\u50a8Python"},"content":{"rendered":"

\"\u5982\u4f55\u5c06\u4e09\u7ef4\u77e9\u9635\u5b58\u50a8Python\"<\/p>\n

\u5728Python\u4e2d\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u6765\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635<\/strong>\u3001\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u5c06\u4e09\u7ef4\u77e9\u9635\u5199\u5165\u6587\u4ef6<\/strong>\u3001\u53ef\u4ee5\u4f7f\u7528pickle\u6a21\u5757\u6765\u4fdd\u5b58\u548c\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528NumPy\u5e93\u662f\u6700\u5e38\u89c1\u548c\u9ad8\u6548\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u65b9\u6cd5\u5728Python\u4e2d\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635\uff0c\u5e76\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u5e93\u5b58\u50a8\u4e09\u7ef4\u77e9\u9635<\/h3>\n<\/p>\n

NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61ndarray\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u77e9\u9635\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u6765\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
matrix_3d = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],<\/p>\n
                      [[10, 11, 12], [13, 14, 15], [16, 17, 18]],<\/p>\n
                      [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])<\/p>\n
print(matrix_3d)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a3x3x3\u7684\u4e09\u7ef4\u77e9\u9635\u3002NumPy\u5e93\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u64cd\u4f5c\u548c\u5904\u7406\u8fd9\u4e9b\u77e9\u9635\uff0c\u4f8b\u5982\u77e9\u9635\u7684\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u3001\u8f6c\u7f6e\u7b49\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5c06\u4e09\u7ef4\u77e9\u9635\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d<\/h3>\n<\/p>\n
NumPy\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5c06\u77e9\u9635\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4f8b\u5982np.save\u3001np.savez\u548cnp.savetxt\u3002\u4e0b\u9762\u6211\u4eec\u5206\u522b\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528np.save\u4fdd\u5b58\u5355\u4e2a\u4e09\u7ef4\u77e9\u9635<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
matrix_3d = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],<\/p>\n
                      [[10, 11, 12], [13, 14, 15], [16, 17, 18]],<\/p>\n
                      [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])<\/p>\n
\u4fdd\u5b58\u4e09\u7ef4\u77e9\u9635\u5230\u6587\u4ef6<\/strong><\/h2>\n
np.save('matrix_3d.npy', matrix_3d)<\/p>\n
\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
loaded_matrix_3d = np.load('matrix_3d.npy')<\/p>\n
print(loaded_matrix_3d)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528np.save\u53ef\u4ee5\u5c06\u5355\u4e2aNumPy\u6570\u7ec4\u4fdd\u5b58\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u8be5\u6587\u4ef6\u4ee5.npy\u4e3a\u540e\u7f00\u3002\u4f7f\u7528np.load\u53ef\u4ee5\u52a0\u8f7d\u8be5\u6587\u4ef6\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528np.savez\u4fdd\u5b58\u591a\u4e2a\u77e9\u9635<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e24\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
matrix_3d_1 = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],<\/p>\n
                        [[10, 11, 12], [13, 14, 15], [16, 17, 18]],<\/p>\n
                        [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])<\/p>\n
matrix_3d_2 = np.array([[[28, 29, 30], [31, 32, 33], [34, 35, 36]],<\/p>\n
                        [[37, 38, 39], [40, 41, 42], [43, 44, 45]],<\/p>\n
                        [[46, 47, 48], [49, 50, 51], [52, 53, 54]]])<\/p>\n
\u4fdd\u5b58\u591a\u4e2a\u4e09\u7ef4\u77e9\u9635\u5230\u6587\u4ef6<\/strong><\/h2>\n
np.savez('matrices_3d.npz', matrix_3d_1=matrix_3d_1, matrix_3d_2=matrix_3d_2)<\/p>\n
\u52a0\u8f7d\u591a\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
loaded_matrices_3d = np.load('matrices_3d.npz')<\/p>\n
print(loaded_matrices_3d['matrix_3d_1'])<\/p>\n
print(loaded_matrices_3d['matrix_3d_2'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528np.savez\u53ef\u4ee5\u5c06\u591a\u4e2aNumPy\u6570\u7ec4\u4fdd\u5b58\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u8be5\u6587\u4ef6\u4ee5.npz\u4e3a\u540e\u7f00\u3002\u4f7f\u7528np.load\u53ef\u4ee5\u52a0\u8f7d\u8be5\u6587\u4ef6\uff0c\u5e76\u901a\u8fc7\u6587\u4ef6\u4e2d\u7684\u952e\u540d\u8bbf\u95ee\u76f8\u5e94\u7684\u6570\u7ec4\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528pickle\u6a21\u5757\u4fdd\u5b58\u548c\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/h3>\n<\/p>\n
pickle\u6a21\u5757\u53ef\u4ee5\u5c06\u4efb\u610fPython\u5bf9\u8c61\u5e8f\u5217\u5316\u5e76\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u4e0b\u9762\u662f\u5982\u4f55\u4f7f\u7528pickle\u6a21\u5757\u4fdd\u5b58\u548c\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pickle<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
matrix_3d = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],<\/p>\n
                      [[10, 11, 12], [13, 14, 15], [16, 17, 18]],<\/p>\n
                      [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])<\/p>\n
\u4fdd\u5b58\u4e09\u7ef4\u77e9\u9635\u5230\u6587\u4ef6<\/strong><\/h2>\n
with open('matrix_3d.pkl', 'wb') as f:<\/p>\n
    pickle.dump(matrix_3d, f)<\/p>\n
\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
with open('matrix_3d.pkl', 'rb') as f:<\/p>\n
    loaded_matrix_3d = pickle.load(f)<\/p>\n
print(loaded_matrix_3d)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528pickle\u6a21\u5757\u53ef\u4ee5\u5c06\u4efb\u610fPython\u5bf9\u8c61\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728\u9700\u8981\u7684\u65f6\u5019\u52a0\u8f7d\u56de\u6765\uff0c\u8fd9\u5bf9\u4e8e\u5b58\u50a8\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528HDF5\u683c\u5f0f\u4fdd\u5b58\u548c\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/h3>\n<\/p>\n
HDF5\u662f\u4e00\u79cd\u7528\u4e8e\u5b58\u50a8\u548c\u7ba1\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u6587\u4ef6\u683c\u5f0f\u3002\u5b83\u5177\u6709\u9ad8\u6548\u7684\u5b58\u50a8\u548c\u8bfb\u53d6\u6027\u80fd\uff0c\u7279\u522b\u9002\u5408\u4e8e\u5b58\u50a8\u5927\u89c4\u6a21\u7684\u79d1\u5b66\u6570\u636e\u3002Python\u4e2d\u53ef\u4ee5\u4f7f\u7528h5py\u5e93\u6765\u64cd\u4f5cHDF5\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import h5py<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
matrix_3d = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],<\/p>\n
                      [[10, 11, 12], [13, 14, 15], [16, 17, 18]],<\/p>\n
                      [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])<\/p>\n
\u4fdd\u5b58\u4e09\u7ef4\u77e9\u9635\u5230HDF5\u6587\u4ef6<\/strong><\/h2>\n
with h5py.File('matrix_3d.h5', 'w') as f:<\/p>\n
    f.create_dataset('matrix_3d', data=matrix_3d)<\/p>\n
\u52a0\u8f7d\u4e09\u7ef4\u77e9\u9635<\/strong><\/h2>\n
with h5py.File('matrix_3d.h5', 'r') as f:<\/p>\n
    loaded_matrix_3d = f['matrix_3d'][:]<\/p>\n
print(loaded_matrix_3d)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528h5py\u5e93\u53ef\u4ee5\u5c06NumPy\u6570\u7ec4\u4fdd\u5b58\u5230HDF5\u6587\u4ef6\u4e2d\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728\u9700\u8981\u7684\u65f6\u5019\u52a0\u8f7d\u56de\u6765\u3002HDF5\u683c\u5f0f\u7279\u522b\u9002\u5408\u4e8e\u9700\u8981\u9891\u7e41\u8bfb\u5199\u5927\u89c4\u6a21\u6570\u636e\u7684\u5e94\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5728Python\u4e2d\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635\u7684\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u4f7f\u7528NumPy\u5e93\u3001pickle\u6a21\u5757\u548cHDF5\u683c\u5f0f\u3002\u4f7f\u7528NumPy\u5e93\u662f\u6700\u5e38\u89c1\u548c\u9ad8\u6548\u7684\u65b9\u6cd5<\/strong>\uff0c\u53ef\u4ee5\u4f7f\u7528np.save\u3001np.savez\u548cnp.savetxt\u5c06\u77e9\u9635\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d<\/strong>\uff0c\u53ef\u4ee5\u4f7f\u7528pickle\u6a21\u5757\u4fdd\u5b58\u548c\u52a0\u8f7d\u4efb\u610fPython\u5bf9\u8c61<\/strong>\uff0c\u53ef\u4ee5\u4f7f\u7528h5py\u5e93\u5c06\u77e9\u9635\u4fdd\u5b58\u5230HDF5\u6587\u4ef6\u4e2d<\/strong>\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5728Python\u4e2d\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u4e09\u7ef4\u77e9\u9635\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u4e09\u7ef4\u77e9\u9635\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5NumPy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip install numpy<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u521b\u5efa\u4e09\u7ef4\u77e9\u9635\u7684\u8bed\u6cd5\u4e3anumpy.array()<\/code>\uff0c\u4f20\u5165\u4e00\u4e2a\u5d4c\u5957\u5217\u8868\u3002\u4f8b\u5982\uff1a<\/p>\n
import numpy as np\nmatrix_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n<\/code><\/pre>\n
\u8fd9\u6837\u5c31\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a2x2x2\u7684\u4e09\u7ef4\u77e9\u9635\u3002<\/p>\n
\u5982\u4f55\u5c06\u4e09\u7ef4\u77e9\u9635\u4fdd\u5b58\u4e3a\u6587\u4ef6\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528NumPy\u7684save()<\/code>\u6216savez()<\/code>\u51fd\u6570\u5c06\u4e09\u7ef4\u77e9\u9635\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002save()<\/code>\u53ef\u4ee5\u4fdd\u5b58\u4e3a.npy<\/code>\u683c\u5f0f\uff0c\u800csavez()<\/code>\u53ef\u4ee5\u4fdd\u5b58\u4e3a.npz<\/code>\u683c\u5f0f\uff0c\u652f\u6301\u591a\u4e2a\u6570\u7ec4\u7684\u5b58\u50a8\u3002\u4f8b\u5982\uff1a<\/p>\n
np.save('matrix_3d.npy', matrix_3d)\n<\/code><\/pre>\n
\u8fd9\u6837\u5c31\u4f1a\u5728\u5f53\u524d\u76ee\u5f55\u4e0b\u751f\u6210\u4e00\u4e2a\u540d\u4e3amatrix_3d.npy<\/code>\u7684\u6587\u4ef6\u3002<\/p>\n
\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u4e09\u7ef4\u77e9\u9635\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528NumPy\u7684load()<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u4e09\u7ef4\u77e9\u9635\u3002\u5bf9\u4e8e\u4f7f\u7528save()<\/code>\u4fdd\u5b58\u7684.npy<\/code>\u6587\u4ef6\uff0c\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\uff1a<\/p>\n
loaded_matrix = np.load('matrix_3d.npy')\n<\/code><\/pre>\n
\u82e5\u662f\u4f7f\u7528savez()<\/code>\u4fdd\u5b58\u7684.npz<\/code>\u6587\u4ef6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u952e\u540d\u6765\u52a0\u8f7d\u7279\u5b9a\u6570\u7ec4\u3002\u8fd9\u6837\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u5b58\u50a8\u7684\u4e09\u7ef4\u77e9\u9635\u6062\u590d\u4e3a\u53d8\u91cf\u4f7f\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528NumPy\u5e93\u6765\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u64cd\u4f5c\u4e09\u7ef4\u77e9\u9635\u3001\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u5c06\u4e09\u7ef4\u77e9\u9635\u5199\u5165\u6587\u4ef6\u3001\u53ef\u4ee5\u4f7f […]","protected":false},"author":3,"featured_media":1118567,"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\/1118562"}],"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=1118562"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118562\/revisions"}],"predecessor-version":[{"id":1118569,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118562\/revisions\/1118569"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1118567"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1118562"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1118562"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1118562"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}