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 thepicklemodule, which is not secure against erroneous or maliciouslyconstructed data. Consider passingallow_pickle=False toload data that is known not to contain object arrays for thesafer handling of untrusted sources.

Parameters:
filefile-like object, string, or pathlib.Path

The file to read. File-like objects must support theseek() andread() methods and must alwaysbe opened in binary mode. Pickled files require that thefile-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 (seenumpy.memmap for a detailed description of the modes). Amemory-mapped array is kept on disk. However, it can be accessedand sliced like any ndarray. Memory mapping is especially usefulfor accessing small fragments of large files without reading theentire file into memory.

allow_picklebool, optional

Allow loading pickled object arrays stored in npy files. Reasons fordisallowing pickles include security, as loading pickled data canexecute arbitrary code. If pickles are disallowed, loading objectarrays will fail. Default: False

fix_importsbool, optional

Only useful when loading Python 2 generated pickled files,which includes npy/npz files containing object arrays. Iffix_importsis True, pickle will try to map the old Python 2 names to the new namesused in Python 3.

encodingstr, optional

What encoding to use when reading Python 2 strings. Only useful whenloading Python 2 generated pickled files, which includesnpy/npz files containing object arrays. Values other than ‘latin1’,‘ASCII’, and ‘bytes’ are not allowed, as they can corrupt numericaldata. Default: ‘ASCII’

max_header_sizeint, optional

Maximum allowed size of the header. Large headers may not be safeto load securely and thus require explicitly passing a larger value.Seeast.literal_eval for details.This option is ignored whenallow_pickle is passed. In that casethe 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 instanceof NpzFile class must be closed to avoid leaking file descriptors.

Raises:
OSError

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

UnpicklingError

Ifallow_pickle=True, but the file cannot be loaded as a pickle.

ValueError

The file contains an object array, butallow_pickle=False given.

EOFError

When callingnp.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 storedin 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 isreturned, containing{filename:array} key-value pairs, one foreach file in the archive.

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

    withload('foo.npz')asdata:a=data['a']

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

Examples

>>>importnumpyasnp

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 rowdirectly from disk:

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