numpy.frombuffer#

numpy.frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)#

Interpret a buffer as a 1-dimensional array.

Parameters:

bufferbuffer_like: An object that exposes the buffer interface.
dtypedata-type, optional: Data-type of the returned array; default: float.
countint, optional: Number of items to read. -1 means all data in the buffer.
offsetint, optional: Start reading the buffer from this offset (in bytes); default: 0.
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.

Returns:

outndarray

See also

ndarray.tobytes: Inverse of this operation, construct Python bytes from the raw data bytes in the array.

Notes

If the buffer has data that is not in machine byte-order, this should be specified as part of the data-type, e.g.:

>>> dt = np.dtype(int)
>>> dt = dt.newbyteorder('>')
>>> np.frombuffer(buf, dtype=dt) 

The data of the resulting array will not be byteswapped, but will be interpreted correctly.

This function creates a view into the original object. This should be safe in general, but it may make sense to copy the result when the original object is mutable or untrusted.

Examples

>>> import numpy as np
>>> s = b'hello world'
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
array([b'w', b'o', b'r', b'l', b'd'], dtype='|S1')

>>> np.frombuffer(b'\x01\x02', dtype=np.uint8)
array([1, 2], dtype=uint8)
>>> np.frombuffer(b'\x01\x02\x03\x04\x05', dtype=np.uint8, count=3)
array([1, 2, 3], dtype=uint8)