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 PathAn 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-typeData 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.
countintNumber of items to read. -1 means all items (i.e., the complete file).
sepstrSeparator 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.
offsetintThe offset (in bytes) from the file’s current position. Defaults to 0. Only permitted for binary files.
likearray_like, optionalReference 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.
Added in version 1.20.0.
Notes
Do not rely on the combination of tofile and
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
and
instead.
Examples
Construct an ndarray:
>>> importnumpyasnp>>> 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>>> xarray([((10, 0), 98.25)], dtype=[('time', [('min', '<i8'), ('sec', '<i8')]), ('temp', '<f8')])Save the raw data to disk:
>>> importtempfile>>> 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')])