numpy.frombuffer#

numpy.frombuffer(buffer, dtype=np.float64, 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 is numpy.float64.
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.

在 1.20.0 版新加入.

Returns:

outndarray

也參考

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(np.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)