numpy.load#

numpy.load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII', *, max_header_size=10000)[source]#

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

Warning

Loading files that contain object arrays uses the pickle module, which is not secure against erroneous or maliciously constructed data. Consider passing allow_pickle=False to load data that is known not to contain object arrays for the safer handling of untrusted sources.

Parameters:

filefile-like object, string, or pathlib.Path: The file to read. File-like objects must support the seek() and read() methods and must always be opened in binary mode. 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.
allow_picklebool, optional: Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: False
fix_importsbool, optional: Only useful when loading Python 2 generated pickled files, which includes npy/npz files containing object arrays. If fix_imports is True, pickle will try to map the old Python 2 names to the new names used in Python 3.
encodingstr, optional: What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files, which includes npy/npz files containing object arrays. Values other than latin1, ASCII, and bytes are not allowed, as they can corrupt numerical data. Default: ASCII
max_header_sizeint, optional: Maximum allowed size of the header. Large headers may not be safe to load securely and thus require explicitly passing a larger value. See ast.literal_eval for details. This option is ignored when allow_pickle is passed. In that case the file is by definition trusted and the limit is unnecessary.

Returns:

resultarray, 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:

OSError: If the input file does not exist or cannot be read.
UnpicklingError: If allow_pickle=True, but the file cannot be loaded as a pickle.
ValueError: The file contains an object array, but allow_pickle=False given.
EOFError: When calling np.load multiple times on the same file handle, if all data has already been read

See also

save, savez, savez_compressed, loadtxt
memmap: Create a memory-map to an array stored in a file on disk.
lib.format.open_memmap: Create or load a memory-mapped .npy file.

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

Try it in your browser!

>>> import numpy as np

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])

Input and output

numpy.save