numpy.load

numpy.load(file, mmap_mode=None)

Load an array(s) or pickled objects from .npy, .npz, or pickled files.

Parameters :

file : file-like object or string

The file to read. It must support seek() and read() methods. If the filename extension is .gz, the file is first decompressed.

mmap_mode: {None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optional :

If not None, then memory-map the file, using the given mode (see numpy.memmap for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory.

Returns :

result : array, tuple, dict, etc.

Data stored in the file. For ‘.npz’ files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors.

Raises :

IOError :

If the input file does not exist or cannot be read.

See also

save, savez, loadtxt

memmap

Create a memory-map to an array stored in a file on disk.

Notes

• If the file contains pickle data, then whatever object is stored in the pickle is returned.

• If the file is a .npy file, then a single array is returned.

• If the file is a .npz file, then a dictionary-like object is returned, containing {filename: array} key-value pairs, one for each file in the archive.

• If the file is a .npz file, the returned value supports the context manager protocol in a similar fashion to the open function:

  with load('foo.npz') as data:
      a = data['a']
  

  The underlyling file descriptor is closed when exiting the ‘with’ block.

Examples

Store data to disk, and load it again:

>>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
       [4, 5, 6]])

Store compressed data to disk, and load it again:

>>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
       [4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()

Mem-map the stored array, and then access the second row directly from disk:

>>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])