{"id":996604,"date":"2024-12-27T09:16:21","date_gmt":"2024-12-27T01:16:21","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/996604.html"},"modified":"2024-12-27T09:16:23","modified_gmt":"2024-12-27T01:16:23","slug":"python%e5%a6%82%e4%bd%95%e6%89%93%e5%bc%80npz%e6%96%87%e4%bb%b6","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/996604.html","title":{"rendered":"python\u5982\u4f55\u6253\u5f00npz\u6587\u4ef6"},"content":{"rendered":"

\"python\u5982\u4f55\u6253\u5f00npz\u6587\u4ef6\"<\/p>\n

\u8981\u6253\u5f00npz\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684NumPy\u5e93\uff0c\u5177\u4f53\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u8bfb\u53d6\u6587\u4ef6\u3001\u901a\u8fc7\u952e\u8bbf\u95ee\u6570\u7ec4\u3001\u7ed3\u5408with<\/code>\u8bed\u53e5\u7ba1\u7406\u6587\u4ef6\u8d44\u6e90\u7b49\u3002<\/strong> \u5176\u4e2d\uff0cnumpy.load<\/code>\u51fd\u6570\u662f\u6700\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u52a0\u8f7dnpz\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u7c7b\u4f3c\u5b57\u5178\u7684\u5bf9\u8c61\u3002\u901a\u8fc7\u8be5\u5bf9\u8c61\uff0c\u53ef\u4ee5\u4f7f\u7528\u952e\u6765\u8bbf\u95ee\u5b58\u50a8\u5728npz\u6587\u4ef6\u4e2d\u7684\u5404\u4e2a\u6570\u7ec4\u3002\u4e3a\u4e86\u66f4\u597d\u5730\u7ba1\u7406\u6587\u4ef6\u8d44\u6e90\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u8f83\u5927\u7684\u6587\u4ef6\u65f6\uff0c\u5efa\u8bae\u4f7f\u7528with<\/code>\u8bed\u53e5\u6765\u786e\u4fdd\u6587\u4ef6\u5728\u4f7f\u7528\u540e\u88ab\u6b63\u786e\u5173\u95ed\u3002<\/p>\n<\/p>\n

\u4e00\u3001NPZ\u6587\u4ef6\u4ecb\u7ecd\u4e0e\u57fa\u672c\u64cd\u4f5c<\/p>\n<\/p>\n

NPZ\u6587\u4ef6\u662f\u4e00\u79cd\u901a\u8fc7NumPy\u5e93\u521b\u5efa\u7684\u6587\u4ef6\u683c\u5f0f\uff0c\u7528\u4e8e\u5b58\u50a8\u591a\u4e2a\u6570\u7ec4\u7684\u6570\u636e\u3002\u5b83\u662f\u57fa\u4e8eNumpy\u7684\u538b\u7f29\u5305\uff0c\u80fd\u591f\u6709\u6548\u5730\u5b58\u50a8\u548c\u4f20\u8f93\u5927\u91cf\u7684\u6570\u503c\u6570\u636e\u3002NPZ\u6587\u4ef6\u4e2d\u6bcf\u4e2a\u6570\u7ec4\u90fd\u6709\u4e00\u4e2a\u552f\u4e00\u7684\u952e\uff0c\u53ef\u4ee5\u901a\u8fc7\u8be5\u952e\u6765\u8bbf\u95ee\u5177\u4f53\u7684\u6570\u7ec4\u6570\u636e\u3002<\/p>\n<\/p>\n

1.1\u3001NPZ\u6587\u4ef6\u7684\u521b\u5efa<\/p>\n<\/p>\n

\u521b\u5efaNPZ\u6587\u4ef6\u7684\u8fc7\u7a0b\u76f8\u5bf9\u7b80\u5355\uff0c\u901a\u5e38\u901a\u8fc7numpy.savez<\/code>\u6216numpy.savez_compressed<\/code>\u51fd\u6570\u6765\u751f\u6210\u3002numpy.savez<\/code>\u53ef\u4ee5\u5c06\u591a\u4e2a\u6570\u7ec4\u4fdd\u5b58\u5230\u4e00\u4e2aNPZ\u6587\u4ef6\u4e2d\uff0c\u800cnumpy.savez_compressed<\/code>\u5219\u4f1a\u5bf9\u6570\u636e\u8fdb\u884c\u538b\u7f29\uff0c\u4ece\u800c\u8282\u7701\u5b58\u50a8\u7a7a\u95f4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e9b\u793a\u4f8b\u6570\u7ec4<\/strong><\/h2>\n
array1 = np.array([1, 2, 3])<\/p>\n
array2 = np.array([4, 5, 6])<\/p>\n
\u4fdd\u5b58\u5230NPZ\u6587\u4ef6<\/strong><\/h2>\n
np.savez('example.npz', arr1=array1, arr2=array2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u8bfb\u53d6NPZ\u6587\u4ef6\u7684\u57fa\u7840\u65b9\u6cd5<\/p>\n<\/p>\n
\u8981\u8bfb\u53d6NPZ\u6587\u4ef6\uff0c\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u3002\u8be5\u51fd\u6570\u4f1a\u8fd4\u56de\u4e00\u4e2a\u7c7b\u4f3c\u5b57\u5178\u7684\u5bf9\u8c61\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u952e\u6765\u8bbf\u95ee\u5177\u4f53\u7684\u6570\u7ec4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u8bfb\u53d6NPZ\u6587\u4ef6\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6NPZ\u6587\u4ef6<\/strong><\/h2>\n
data = np.load('example.npz')<\/p>\n
\u8bbf\u95ee\u6570\u7ec4<\/strong><\/h2>\n
array1 = data['arr1']<\/p>\n
array2 = data['arr2']<\/p>\n
print(array1)  # \u8f93\u51fa: [1 2 3]<\/p>\n
print(array2)  # \u8f93\u51fa: [4 5 6]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7ba1\u7406\u6587\u4ef6\u8d44\u6e90\u4e0e\u6700\u4f73\u5b9e\u8df5<\/p>\n<\/p>\n
\u5728\u5904\u7406\u8f83\u5927\u7684NPZ\u6587\u4ef6\u65f6\uff0c\u5408\u7406\u7ba1\u7406\u6587\u4ef6\u8d44\u6e90\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u4f7f\u7528with<\/code>\u8bed\u53e5\u53ef\u4ee5\u786e\u4fdd\u6587\u4ef6\u5728\u4f7f\u7528\u540e\u88ab\u6b63\u786e\u5173\u95ed\uff0c\u4ece\u800c\u91ca\u653e\u7cfb\u7edf\u8d44\u6e90\u3002<\/p>\n<\/p>\n
2.1\u3001\u4f7f\u7528with<\/code>\u8bed\u53e5\u8bfb\u53d6NPZ\u6587\u4ef6<\/p>\n<\/p>\n
with<\/code>\u8bed\u53e5\u80fd\u591f\u81ea\u52a8\u5904\u7406\u6587\u4ef6\u7684\u6253\u5f00\u548c\u5173\u95ed\uff0c\u4f7f\u4ee3\u7801\u66f4\u52a0\u7b80\u6d01\u548c\u5b89\u5168\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528with<\/code>\u8bed\u53e5\u6765\u8bfb\u53d6NPZ\u6587\u4ef6\u7684\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u4f7f\u7528with\u8bed\u53e5\u8bfb\u53d6NPZ\u6587\u4ef6<\/strong><\/h2>\n
with np.load('example.npz') as data:<\/p>\n
    array1 = data['arr1']<\/p>\n
    array2 = data['arr2']<\/p>\n
    print(array1)  # \u8f93\u51fa: [1 2 3]<\/p>\n
    print(array2)  # \u8f93\u51fa: [4 5 6]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u5904\u7406\u5927\u578b\u6570\u636e\u96c6<\/p>\n<\/p>\n
\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\uff0c\u53ef\u80fd\u9700\u8981\u8003\u8651\u5185\u5b58\u7ba1\u7406\u548c\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u3002\u53ef\u4ee5\u4f7f\u7528\u5206\u6279\u52a0\u8f7d\u6570\u636e\u6216\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u6280\u672f\uff08memory mapping\uff09\u6765\u5904\u7406\u8d85\u5927\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u4e09\u3001NPZ\u6587\u4ef6\u7684\u9ad8\u7ea7\u64cd\u4f5c<\/p>\n<\/p>\n
\u9664\u4e86\u57fa\u672c\u7684\u8bfb\u53d6\u548c\u5199\u5165\u64cd\u4f5c\uff0cNumPy\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u7279\u6027\uff0c\u53ef\u4ee5\u5728\u5904\u7406NPZ\u6587\u4ef6\u65f6\u4f7f\u7528\u3002<\/p>\n<\/p>\n
3.1\u3001\u538b\u7f29\u4e0e\u89e3\u538b\u7f29<\/p>\n<\/p>\n
\u5982\u524d\u6587\u6240\u8ff0\uff0cnumpy.savez_compressed<\/code>\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u538b\u7f29\u3002\u538b\u7f29\u540e\u7684\u6587\u4ef6\u5728\u8bfb\u53d6\u65f6\u4f1a\u81ea\u52a8\u89e3\u538b\u7f29\uff0c\u8fd9\u4f7f\u5f97\u5b58\u50a8\u548c\u4f20\u8f93\u66f4\u52a0\u9ad8\u6548\uff0c\u800c\u4e0d\u4f1a\u589e\u52a0\u8bfb\u53d6\u7684\u590d\u6742\u6027\u3002<\/p>\n<\/p>\n
3.2\u3001\u6570\u636e\u7684\u66f4\u65b0\u4e0e\u91cd\u5199<\/p>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u66f4\u65b0NPZ\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u3002\u7531\u4e8eNPZ\u6587\u4ef6\u7684\u7ed3\u6784\uff0c\u65e0\u6cd5\u76f4\u63a5\u4fee\u6539\u5176\u4e2d\u7684\u6570\u7ec4\uff0c\u56e0\u6b64\u901a\u5e38\u9700\u8981\u8bfb\u53d6\u539f\u59cb\u6570\u636e\uff0c\u66f4\u65b0\u540e\u518d\u4fdd\u5b58\u6210\u65b0\u7684NPZ\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u539f\u59cb\u6570\u636e<\/strong><\/h2>\n
with np.load('example.npz') as data:<\/p>\n
    array1 = data['arr1']<\/p>\n
    array2 = data['arr2']<\/p>\n
\u66f4\u65b0\u6570\u636e<\/strong><\/h2>\n
array1 += 10<\/p>\n
\u4fdd\u5b58\u66f4\u65b0\u540e\u7684\u6570\u636e<\/strong><\/h2>\n
np.savez('example_updated.npz', arr1=array1, arr2=array2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001NPZ\u6587\u4ef6\u5728\u6570\u636e\u79d1\u5b66\u4e2d\u7684\u5e94\u7528<\/p>\n<\/p>\n
NPZ\u6587\u4ef6\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5c24\u5176\u662f\u5728\u9700\u8981\u5904\u7406\u5927\u91cf\u6570\u503c\u6570\u636e\u7684\u60c5\u51b5\u4e0b\u3002<\/p>\n<\/p>\n
4.1\u3001\u6570\u636e\u5b58\u50a8\u4e0e\u5171\u4eab<\/p>\n<\/p>\n
NPZ\u6587\u4ef6\u4fbf\u4e8e\u5b58\u50a8\u548c\u5171\u4eab\u6570\u636e\u96c6\uff0c\u7279\u522b\u662f\u5728\u673a\u5668\u5b66\u4e60<\/a>\u548c\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u53ef\u4ee5\u5c06\u8bad\u7ec3\u6570\u636e\u3001\u6d4b\u8bd5\u6570\u636e\u548c\u6a21\u578b\u53c2\u6570\u7b49\u4fdd\u5b58\u5728\u4e00\u4e2aNPZ\u6587\u4ef6\u4e2d\uff0c\u65b9\u4fbf\u56e2\u961f\u534f\u4f5c\u548c\u6570\u636e\u7ba1\u7406\u3002<\/p>\n<\/p>\n
4.2\u3001\u9884\u5904\u7406\u4e0e\u7279\u5f81\u5de5\u7a0b<\/p>\n<\/p>\n
\u5728\u6570\u636e\u9884\u5904\u7406\u548c\u7279\u5f81\u5de5\u7a0b\u9636\u6bb5\uff0c\u4f7f\u7528NPZ\u6587\u4ef6\u53ef\u4ee5\u8f7b\u677e\u4fdd\u5b58\u4e2d\u95f4\u7ed3\u679c\uff0c\u4ece\u800c\u907f\u514d\u91cd\u590d\u8ba1\u7b97\uff0c\u63d0\u9ad8\u5904\u7406\u6548\u7387\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5c06\u5904\u7406\u540e\u7684\u7279\u5f81\u6570\u636e\u4fdd\u5b58\u4e3aNPZ\u6587\u4ef6\uff0c\u4ee5\u4fbf\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u4f7f\u7528\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
Python\u4e2d\u7684NumPy\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u6765\u5904\u7406NPZ\u6587\u4ef6\u3002\u901a\u8fc7numpy.load<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6NPZ\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff0c\u5e76\u4f7f\u7528with<\/code>\u8bed\u53e5\u6765\u7ba1\u7406\u6587\u4ef6\u8d44\u6e90\u3002\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u538b\u7f29\u6280\u672f\u548c\u5185\u5b58\u6620\u5c04\u6280\u672f\u6765\u4f18\u5316\u6027\u80fd\u3002NPZ\u6587\u4ef6\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u5b58\u50a8\u3001\u5171\u4eab\u548c\u5904\u7406\u5927\u91cf\u6570\u503c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6npz\u6587\u4ef6\u7684\u5185\u5bb9\uff1f<\/strong>
\u8981\u8bfb\u53d6npz\u6587\u4ef6\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.load()<\/code>\u51fd\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u6587\u4ef6\u4e2d\u6240\u6709\u6570\u7ec4\u7684\u5b57\u5178\u5bf9\u8c61\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u952e\u6765\u8bbf\u95ee\u8fd9\u4e9b\u6570\u7ec4\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import numpy as np\n\ndata = np.load('file.npz')\nprint(data.files)  # \u5217\u51fa\u6587\u4ef6\u4e2d\u6240\u6709\u6570\u7ec4\u7684\u540d\u79f0\narray1 = data['array_name']  # \u901a\u8fc7\u540d\u79f0\u83b7\u53d6\u7279\u5b9a\u6570\u7ec4\n<\/code><\/pre>\n
npz\u6587\u4ef6\u4e2d\u5b58\u50a8\u7684\u6570\u636e\u7c7b\u578b\u6709\u54ea\u4e9b\uff1f<\/strong>
npz\u6587\u4ef6\u662f\u4e00\u79cd\u538b\u7f29\u6587\u4ef6\u683c\u5f0f\uff0c\u53ef\u4ee5\u5b58\u50a8\u591a\u4e2a\u6570\u7ec4\u3002\u6bcf\u4e2a\u6570\u7ec4\u53ef\u4ee5\u662f\u4e0d\u540c\u7684\u7ef4\u5ea6\u548c\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5e03\u5c14\u503c\u7b49\u3002\u4f7f\u7528NumPy\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u65f6\uff0c\u60a8\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n
\u6253\u5f00npz\u6587\u4ef6\u65f6\u4f1a\u9047\u5230\u54ea\u4e9b\u5e38\u89c1\u95ee\u9898\uff1f<\/strong>
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