numpy.fromfile#

numpy.fromfile(file, dtype=np.float64, 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

An open file object, a string containing the filename, or a Path object. When reading from a file object it must support random access (i.e. it must have tell and seek methods).

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.

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 files current position. Defaults to 0. Only permitted for binary files.

likearray_like, optional

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.

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

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Construct an ndarray:

>>> import numpy as np
>>> 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')])

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