numpy.load

numpy.load(file, mmap_mode=None)

Load arrays or pickled objects from .npy, .npz or pickled files.

Parameters:

file : file-like object or string

The file to read. File-like objects must support the seek() and read() methods. Pickled files require that the file-like object support the readline() method as well.

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, savez_compressed, 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 underlying 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])