numpy.fromfile¶

numpy.fromfile(file, dtype=float, count=-1, sep='', offset=0, *, like=None)¶

Construct an array from data in a text or binary file.

A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function.

Parameters

filefile or str or Path: Open file object or filename.

Changed in version 1.17.0: pathlib.Path objects are now accepted.
dtypedata-type: Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. Most builtin numeric types are supported and extension types may be supported.

New in version 1.18.0: Complex dtypes.
countint: Number of items to read. -1 means all items (i.e., the complete file).
sepstr: Separator between items if file is a text file. Empty (“”) separator means the file should be treated as binary. Spaces (” “) in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace.
offsetint: The offset (in bytes) from the file’s current position. Defaults to 0. Only permitted for binary files.

New in version 1.17.0.
likearray_like: Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

Note

The like keyword is an experimental feature pending on acceptance of NEP 35.

New in version 1.20.0.

See also

load, save
ndarray.tofile
loadtxt: More flexible way of loading data from a text file.

Notes

Do not rely on the combination of tofile and fromfile for data storage, as the binary files generated are not platform independent. In particular, no byte-order or data-type information is saved. Data can be stored in the platform independent .npy format using save and load instead.

Examples

Construct an ndarray:

>>> dt = np.dtype([('time', [('min', np.int64), ('sec', np.int64)]),
...                ('temp', float)])
>>> x = np.zeros((1,), dtype=dt)
>>> x['time']['min'] = 10; x['temp'] = 98.25
>>> x
array([((10, 0), 98.25)],
      dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])

Save the raw data to disk:

>>> import tempfile
>>> fname = tempfile.mkstemp()[1]
>>> x.tofile(fname)

Read the raw data from disk:

>>> np.fromfile(fname, dtype=dt)
array([((10, 0), 98.25)],
      dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])

The recommended way to store and load data:

>>> np.save(fname, x)
>>> np.load(fname + '.npy')
array([((10, 0), 98.25)],
      dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])

numpy.frombuffer numpy.fromfunction