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 passingallow_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()
andread()
methods and must always be opened in binary mode. Pickled files require that the file-like object support thereadline()
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
Changed in version 1.16.3: Made default False in response to CVE-2019-6446.
- fix_importsbool, optional
Only useful when loading Python 2 generated pickled files on Python 3, 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 in Python 3, 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
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])