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 file’s 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.

在 1.20.0 版本加入.

参见

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:

>>> 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')])