{"id":1154269,"date":"2025-01-13T17:49:14","date_gmt":"2025-01-13T09:49:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1154269.html"},"modified":"2025-01-13T17:49:16","modified_gmt":"2025-01-13T09:49:16","slug":"python%e5%a6%82%e4%bd%95%e4%bf%9d%e5%ad%98npy%e6%96%87%e4%bb%b6-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1154269.html","title":{"rendered":"python\u5982\u4f55\u4fdd\u5b58npy\u6587\u4ef6"},"content":{"rendered":"

\"python\u5982\u4f55\u4fdd\u5b58npy\u6587\u4ef6\"<\/p>\n

\u4f7f\u7528numpy<\/code>\u5e93\u4e2d\u7684save<\/code>\u51fd\u6570\u3001savez<\/code>\u51fd\u6570\u3001savez_compressed<\/code>\u51fd\u6570\u53ef\u4ee5\u4fdd\u5b58npy\u6587\u4ef6<\/strong>\u3002\u5176\u4e2d\uff0csave<\/code>\u51fd\u6570\u7528\u4e8e\u4fdd\u5b58\u5355\u4e2a\u6570\u7ec4\uff0csavez<\/code>\u51fd\u6570\u7528\u4e8e\u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4\uff0c\u800csavez_compressed<\/code>\u51fd\u6570\u5219\u662f\u5728\u4fdd\u5b58\u65f6\u5bf9\u6587\u4ef6\u8fdb\u884c\u538b\u7f29\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u4fdd\u5b58npy\u6587\u4ef6\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5355\u4e2a\u6570\u7ec4\u7684\u4fdd\u5b58<\/h3>\n<\/p>\n

\u4f7f\u7528save<\/code>\u51fd\u6570<\/h4>\n<\/p>\n

numpy.save<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u4fdd\u5b58\u5355\u4e2a\u6570\u7ec4\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u6587\u4ef6\u7684\u6269\u5c55\u540d\u9ed8\u8ba4\u662f.npy<\/code>\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

    \n
  1. \u5bfc\u5165\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165numpy<\/code>\u5e93\u3002<\/li>\n
  2. \u521b\u5efa\u6570\u7ec4<\/strong>\uff1a\u751f\u6210\u9700\u8981\u4fdd\u5b58\u7684\u6570\u7ec4\u3002<\/li>\n
  3. \u4fdd\u5b58\u6570\u7ec4<\/strong>\uff1a\u4f7f\u7528numpy.save<\/code>\u51fd\u6570\u5c06\u6570\u7ec4\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002<\/li>\n<\/ol>\n

    import numpy as np<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
    array = np.array([1, 2, 3, 4, 5])<\/p>\n
    \u4fdd\u5b58\u6570\u7ec4\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
    np.save('array.npy', array)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6570\u7ec4array<\/code>\u5c06\u88ab\u4fdd\u5b58\u5230\u4e00\u4e2a\u540d\u4e3aarray.npy<\/code>\u7684\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
    \u4e8c\u3001\u591a\u4e2a\u6570\u7ec4\u7684\u4fdd\u5b58<\/h3>\n<\/p>\n
    \u4f7f\u7528savez<\/code>\u51fd\u6570<\/h4>\n<\/p>\n
    numpy.savez<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d\uff0c\u6587\u4ef6\u7684\u6269\u5c55\u540d\u9ed8\u8ba4\u662f.npz<\/code>\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      \n
    1. \u5bfc\u5165\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165numpy<\/code>\u5e93\u3002<\/li>\n
    2. \u521b\u5efa\u6570\u7ec4<\/strong>\uff1a\u751f\u6210\u9700\u8981\u4fdd\u5b58\u7684\u591a\u4e2a\u6570\u7ec4\u3002<\/li>\n
    3. \u4fdd\u5b58\u6570\u7ec4<\/strong>\uff1a\u4f7f\u7528numpy.savez<\/code>\u51fd\u6570\u5c06\u591a\u4e2a\u6570\u7ec4\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002<\/li>\n<\/ol>\n
      import numpy as np<\/p>\n
      \u521b\u5efa\u591a\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
      array1 = np.array([1, 2, 3, 4, 5])<\/p>\n
      array2 = np.array([6, 7, 8, 9, 10])<\/p>\n
      \u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4\u5230\u4e00\u4e2a\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
      np.savez('arrays.npz', array1=array1, array2=array2)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6570\u7ec4array1<\/code>\u548carray2<\/code>\u5c06\u88ab\u4fdd\u5b58\u5230\u4e00\u4e2a\u540d\u4e3aarrays.npz<\/code>\u7684\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
      \u4f7f\u7528savez_compressed<\/code>\u51fd\u6570<\/h4>\n<\/p>\n
      numpy.savez_compressed<\/code>\u51fd\u6570\u4e0enumpy.savez<\/code>\u51fd\u6570\u7684\u529f\u80fd\u7c7b\u4f3c\uff0c\u53ea\u4e0d\u8fc7\u5b83\u4f1a\u5bf9\u6587\u4ef6\u8fdb\u884c\u538b\u7f29\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
        \n
      1. \u5bfc\u5165\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165numpy<\/code>\u5e93\u3002<\/li>\n
      2. \u521b\u5efa\u6570\u7ec4<\/strong>\uff1a\u751f\u6210\u9700\u8981\u4fdd\u5b58\u7684\u591a\u4e2a\u6570\u7ec4\u3002<\/li>\n
      3. \u4fdd\u5b58\u6570\u7ec4<\/strong>\uff1a\u4f7f\u7528numpy.savez_compressed<\/code>\u51fd\u6570\u5c06\u591a\u4e2a\u6570\u7ec4\u4fdd\u5b58\u5230\u538b\u7f29\u6587\u4ef6\u4e2d\u3002<\/li>\n<\/ol>\n
        import numpy as np<\/p>\n
        \u521b\u5efa\u591a\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
        array1 = np.array([1, 2, 3, 4, 5])<\/p>\n
        array2 = np.array([6, 7, 8, 9, 10])<\/p>\n
        \u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4\u5230\u4e00\u4e2a\u538b\u7f29\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
        np.savez_compressed('arrays_compressed.npz', array1=array1, array2=array2)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6570\u7ec4array1<\/code>\u548carray2<\/code>\u5c06\u88ab\u4fdd\u5b58\u5230\u4e00\u4e2a\u540d\u4e3aarrays_compressed.npz<\/code>\u7684\u538b\u7f29\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
        \u4e09\u3001\u4ecenpy\u6587\u4ef6\u8bfb\u53d6\u6570\u7ec4<\/h3>\n<\/p>\n
        \u4fdd\u5b58\u6570\u7ec4\u4e4b\u540e\uff0c\u53ef\u80fd\u9700\u8981\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u7ec4\u8fdb\u884c\u5904\u7406\u3002numpy<\/code>\u63d0\u4f9b\u4e86\u76f8\u5e94\u7684\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \u4f7f\u7528load<\/code>\u51fd\u6570\u8bfb\u53d6\u5355\u4e2a\u6570\u7ec4<\/h4>\n<\/p>\n
        numpy.load<\/code>\u51fd\u6570\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6\u4fdd\u5b58\u7684\u5355\u4e2a\u6570\u7ec4\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
          \n
        1. \u5bfc\u5165\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165numpy<\/code>\u5e93\u3002<\/li>\n
        2. \u8bfb\u53d6\u6570\u7ec4<\/strong>\uff1a\u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u7ec4\u3002<\/li>\n<\/ol>\n
          import numpy as np<\/p>\n
          \u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u7ec4<\/strong><\/h2>\n
          array = np.load('array.npy')<\/p>\n
          print(array)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6570\u7ec4array<\/code>\u5c06\u88ab\u4ecearray.npy<\/code>\u6587\u4ef6\u4e2d\u8bfb\u53d6\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
          \u4f7f\u7528load<\/code>\u51fd\u6570\u8bfb\u53d6\u591a\u4e2a\u6570\u7ec4<\/h4>\n<\/p>\n
          numpy.load<\/code>\u51fd\u6570\u540c\u6837\u53ef\u4ee5\u7528\u6765\u8bfb\u53d6\u4fdd\u5b58\u7684\u591a\u4e2a\u6570\u7ec4\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u6b65\u9aa4\u548c\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
            \n
          1. \u5bfc\u5165\u5e93<\/strong>\uff1a\u9996\u5148\u9700\u8981\u5bfc\u5165numpy<\/code>\u5e93\u3002<\/li>\n
          2. \u8bfb\u53d6\u6570\u7ec4<\/strong>\uff1a\u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u591a\u4e2a\u6570\u7ec4\u3002<\/li>\n<\/ol>\n
            import numpy as np<\/p>\n
            \u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u591a\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
            data = np.load('arrays.npz')<\/p>\n
            \u83b7\u53d6\u6bcf\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
            array1 = data['array1']<\/p>\n
            array2 = data['array2']<\/p>\n
            print(array1)<\/p>\n
            print(array2)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6570\u7ec4array1<\/code>\u548carray2<\/code>\u5c06\u88ab\u4ecearrays.npz<\/code>\u6587\u4ef6\u4e2d\u8bfb\u53d6\u5e76\u6253\u5370\u51fa\u6765\u3002<\/p>\n<\/p>\n
            \u56db\u3001\u4fdd\u5b58\u548c\u8bfb\u53d6\u590d\u6742\u6570\u636e\u7c7b\u578b<\/h3>\n<\/p>\n
            \u6709\u65f6\u5019\uff0c\u4fdd\u5b58\u548c\u8bfb\u53d6\u590d\u6742\u7684\u6570\u636e\u7c7b\u578b\uff08\u5982\u81ea\u5b9a\u4e49\u7684\u7ed3\u6784\u5316\u6570\u7ec4\uff09\u4e5f\u975e\u5e38\u91cd\u8981\u3002numpy<\/code>\u5e93\u540c\u6837\u63d0\u4f9b\u4e86\u5de5\u5177\u6765\u5904\u7406\u8fd9\u4e9b\u60c5\u51b5\u3002<\/p>\n<\/p>\n
            \u4fdd\u5b58\u7ed3\u6784\u5316\u6570\u7ec4<\/h4>\n<\/p>\n
            \u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u7684\u7ed3\u6784\u5316\u6570\u7ec4\uff0c\u5e76\u4f7f\u7528numpy.save<\/code>\u6216numpy.savez<\/code>\u51fd\u6570\u4fdd\u5b58\u5b83\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            \u521b\u5efa\u4e00\u4e2a\u7ed3\u6784\u5316\u6570\u7ec4<\/strong><\/h2>\n
            dtype = [('name', 'S10'), ('age', 'i4'), ('score', 'f4')]<\/p>\n
            values = [('Alice', 25, 85.5), ('Bob', 30, 92.0), ('Charlie', 35, 77.0)]<\/p>\n
            structured_array = np.array(values, dtype=dtype)<\/p>\n
            \u4fdd\u5b58\u7ed3\u6784\u5316\u6570\u7ec4\u5230\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
            np.save('structured_array.npy', structured_array)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8bfb\u53d6\u7ed3\u6784\u5316\u6570\u7ec4<\/h4>\n<\/p>\n
            \u4f7f\u7528numpy.load<\/code>\u51fd\u6570\u53ef\u4ee5\u8bfb\u53d6\u4fdd\u5b58\u7684\u7ed3\u6784\u5316\u6570\u7ec4\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            \u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u7ed3\u6784\u5316\u6570\u7ec4<\/strong><\/h2>\n
            structured_array = np.load('structured_array.npy')<\/p>\n
            print(structured_array)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e94\u3001\u5176\u4ed6\u9ad8\u7ea7\u7528\u6cd5<\/h3>\n<\/p>\n
            \u5728\u4fdd\u5b58\u548c\u8bfb\u53d6\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u8fd8\u9700\u8981\u4e00\u4e9b\u9ad8\u7ea7\u7528\u6cd5\uff0c\u6bd4\u5982\u4fdd\u5b58\u548c\u8bfb\u53d6\u591a\u7ef4\u6570\u7ec4\uff0c\u5904\u7406\u5927\u6587\u4ef6\uff0c\u4ee5\u53ca\u4e0e\u5176\u4ed6\u6587\u4ef6\u683c\u5f0f\u7684\u8f6c\u6362\u7b49\u3002<\/p>\n<\/p>\n
            \u4fdd\u5b58\u548c\u8bfb\u53d6\u591a\u7ef4\u6570\u7ec4<\/h4>\n<\/p>\n
            \u591a\u7ef4\u6570\u7ec4\u7684\u4fdd\u5b58\u548c\u8bfb\u53d6\u4e0e\u4e00\u7ef4\u6570\u7ec4\u7c7b\u4f3c\uff0c\u53ea\u9700\u8981\u521b\u5efa\u591a\u7ef4\u6570\u7ec4\u5e76\u4f7f\u7528\u76f8\u5e94\u7684\u51fd\u6570\u8fdb\u884c\u4fdd\u5b58\u548c\u8bfb\u53d6\u5373\u53ef\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            \u521b\u5efa\u4e00\u4e2a\u591a\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
            multi_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])<\/p>\n
            \u4fdd\u5b58\u591a\u7ef4\u6570\u7ec4\u5230\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
            np.save('multi_array.npy', multi_array)<\/p>\n
            \u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u591a\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
            multi_array_loaded = np.load('multi_array.npy')<\/p>\n
            print(multi_array_loaded)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5904\u7406\u5927\u6587\u4ef6<\/h4>\n<\/p>\n
            \u5bf9\u4e8e\u975e\u5e38\u5927\u7684\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u5b58\u6620\u5c04\uff08memory-mapping\uff09\u6280\u672f\uff0c\u907f\u514d\u5c06\u6574\u4e2a\u6587\u4ef6\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\u3002\u8fd9\u5bf9\u4e8e\u5904\u7406\u5927\u6570\u636e\u96c6\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            \u521b\u5efa\u4e00\u4e2a\u5927\u7684\u6570\u7ec4<\/strong><\/h2>\n
            large_array = np.random.rand(10000, 10000)<\/p>\n
            \u4fdd\u5b58\u5927\u6570\u7ec4\u5230\u6587\u4ef6\u4e2d<\/strong><\/h2>\n
            np.save('large_array.npy', large_array)<\/p>\n
            \u4f7f\u7528\u5185\u5b58\u6620\u5c04\u8bfb\u53d6\u5927\u6570\u7ec4<\/strong><\/h2>\n
            large_array_mmap = np.load('large_array.npy', mmap_mode='r')<\/p>\n
            print(large_array_mmap.shape)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e0e\u5176\u4ed6\u6587\u4ef6\u683c\u5f0f\u7684\u8f6c\u6362<\/h4>\n<\/p>\n
            \u6709\u65f6\u5019\u9700\u8981\u5c06npy\u6587\u4ef6\u8f6c\u6362\u4e3a\u5176\u4ed6\u683c\u5f0f\uff0c\u5982CSV\u6216HDF5\u3002\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u5e93\uff08\u5982pandas<\/code>\u6216h5py<\/code>\uff09\u6765\u5b9e\u73b0\u8fd9\u4e9b\u8f6c\u6362\u3002<\/p>\n<\/p>\n
            import numpy as np<\/p>\n
            import pandas as pd<\/p>\n
            \u4ecenpy\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u7ec4<\/strong><\/h2>\n
            array = np.load('array.npy')<\/p>\n
            \u5c06\u6570\u7ec4\u8f6c\u6362\u4e3aDataFrame\u5e76\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6<\/strong><\/h2>\n
            df = pd.DataFrame(array)<\/p>\n
            df.to_csv('array.csv', index=False)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
            \u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u5df2\u7ecf\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528numpy<\/code>\u5e93\u4fdd\u5b58\u548c\u8bfb\u53d6npy\u6587\u4ef6\u7684\u5404\u79cd\u65b9\u6cd5\u548c\u6280\u5de7\u3002\u4f7f\u7528save<\/code>\u51fd\u6570\u3001savez<\/code>\u51fd\u6570\u3001savez_compressed<\/code>\u51fd\u6570\u53ef\u4ee5\u4fdd\u5b58npy\u6587\u4ef6<\/strong>\uff0c\u5176\u4e2dsave<\/code>\u51fd\u6570\u7528\u4e8e\u4fdd\u5b58\u5355\u4e2a\u6570\u7ec4\uff0csavez<\/code>\u51fd\u6570\u7528\u4e8e\u4fdd\u5b58\u591a\u4e2a\u6570\u7ec4\uff0c\u800csavez_compressed<\/code>\u51fd\u6570\u5219\u662f\u5728\u4fdd\u5b58\u65f6\u5bf9\u6587\u4ef6\u8fdb\u884c\u538b\u7f29\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u6211\u4eec\u8fd8\u8ba8\u8bba\u4e86\u5982\u4f55\u4fdd\u5b58\u548c\u8bfb\u53d6\u7ed3\u6784\u5316\u6570\u7ec4\u3001\u591a\u7ef4\u6570\u7ec4\uff0c\u5904\u7406\u5927\u6587\u4ef6\uff0c\u4ee5\u53ca\u4e0e\u5176\u4ed6\u6587\u4ef6\u683c\u5f0f\u7684\u8f6c\u6362\u7b49\u9ad8\u7ea7\u7528\u6cd5\u3002\u5e0c\u671b\u8fd9\u4e9b\u5185\u5bb9\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528npy\u6587\u4ef6\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u548c\u4fdd\u5b58npy\u6587\u4ef6\uff1f<\/strong>
            \u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efa\u548c\u4fdd\u5b58npy\u6587\u4ef6\u3002\u9996\u5148\uff0c\u786e\u4fdd\u60a8\u5df2\u5b89\u88c5NumPy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528pip install numpy<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002\u7136\u540e\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4\u5e76\u5c06\u5176\u4fdd\u5b58\u4e3anpy\u6587\u4ef6\uff1a<\/p>\n
            import numpy as np\n\n# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u7ec4\narray = np.array([[1, 2, 3], [4, 5, 6]])\n\n# \u4fdd\u5b58\u4e3anpy\u6587\u4ef6\nnp.save('example.npy', array)\n<\/code><\/pre>\n
            \u8fd9\u6bb5\u4ee3\u7801\u4f1a\u5728\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55\u4e0b\u751f\u6210\u4e00\u4e2a\u540d\u4e3aexample.npy<\/code>\u7684\u6587\u4ef6\u3002<\/p>\n
            npy\u6587\u4ef6\u4fdd\u5b58\u8fc7\u7a0b\u4e2d\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
            \u4fdd\u5b58npy\u6587\u4ef6\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u548c\u6570\u7ec4\u7ef4\u5ea6\u662f\u60a8\u6240\u9700\u7684\u3002NumPy\u4f1a\u81ea\u52a8\u5904\u7406\u6570\u636e\u7c7b\u578b\uff0c\u4f46\u5982\u679c\u60a8\u5e0c\u671b\u4ee5\u7279\u5b9a\u683c\u5f0f\u4fdd\u5b58\u6570\u636e\uff0c\u53ef\u4ee5\u5728\u521b\u5efa\u6570\u7ec4\u65f6\u6307\u5b9a\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982dtype=np.float32<\/code>\u3002\u6b64\u5916\uff0c\u4fdd\u5b58\u7684\u6587\u4ef6\u540d\u5e94\u907f\u514d\u4f7f\u7528\u7279\u6b8a\u5b57\u7b26\uff0c\u4ee5\u786e\u4fdd\u6587\u4ef6\u80fd\u591f\u6b63\u5e38\u8bfb\u53d6\u3002<\/p>\n
            \u5982\u4f55\u52a0\u8f7dnpy\u6587\u4ef6\u5e76\u67e5\u770b\u5176\u5185\u5bb9\uff1f<\/strong>
            \u52a0\u8f7dnpy\u6587\u4ef6\u540c\u6837\u7b80\u5355\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u7684load<\/code>\u51fd\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f8b\u5b50\uff1a<\/p>\n
            loaded_array = np.load('example.npy')\nprint(loaded_array)\n<\/code><\/pre>\n
            \u8fd9\u6bb5\u4ee3\u7801\u5c06\u8bfb\u53d6example.npy<\/code>\u6587\u4ef6\uff0c\u5e76\u6253\u5370\u51fa\u5176\u5185\u5bb9\u3002\u786e\u4fdd\u6587\u4ef6\u8def\u5f84\u6b63\u786e\uff0c\u4ee5\u907f\u514d\u6587\u4ef6\u672a\u627e\u5230\u7684\u9519\u8bef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528numpy\u5e93\u4e2d\u7684save\u51fd\u6570\u3001savez\u51fd\u6570\u3001savez_compressed\u51fd\u6570\u53ef\u4ee5\u4fdd\u5b58npy\u6587\u4ef6\u3002\u5176\u4e2d […]","protected":false},"author":3,"featured_media":1154273,"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\/1154269"}],"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=1154269"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154269\/revisions"}],"predecessor-version":[{"id":1154278,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1154269\/revisions\/1154278"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1154273"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1154269"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1154269"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1154269"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}