NumPy-frombuffer和fromstring有什么区别? [英] NumPy - What is the difference between frombuffer and fromstring?

问题描述

他们似乎给我同样的结果:

They appear to give the same result to me:

In [32]: s
Out[32]: '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'

In [27]: np.frombuffer(s, dtype="int8")
Out[27]:
array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0, 21,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], dtype=int8)

In [28]: np.fromstring(s, dtype="int8")
Out[28]:
array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0, 21,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], dtype=int8)

In [33]: b = buffer(s)

In [34]: b
Out[34]: <read-only buffer for 0x035F8020, size -1, offset 0 at 0x036F13A0>

In [35]: np.fromstring(b, dtype="int8")
Out[35]:
array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0, 21,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], dtype=int8)

In [36]: np.frombuffer(b, dtype="int8")
Out[36]:
array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0, 21,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
    0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0], dtype=int8)

什么时候应该使用另一个?

When should one be used vs. the other?

推荐答案

从实际的角度来看，区别在于:

From a practical standpoint, the difference is that:

x = np.fromstring(s, dtype='int8')

将在内存中复制字符串，同时:

Will make a copy of the string in memory, while:

x = np.frombuffer(s, dtype='int8')

或

x = np.frombuffer(buffer(s), dtype='int8')

将直接使用字符串的内存缓冲区，并且不会使用任何*额外的内存.如果buffer的输入是字符串，则使用frombuffer还将导致只读数组，因为字符串在python中是不可变的.

Will use the memory buffer of the string directly and won't use any* additional memory. Using frombuffer will also result in a read-only array if the input to buffer is a string, as strings are immutable in python.

(* *忽略了用于其他python ndarray对象的几个字节的内存-数据的基础内存将被共享.)

(*Neglecting a few bytes of memory used for an additional python ndarray object -- The underlying memory for the data will be shared.)

如果您不熟悉 buffer对象(python3.x中的memoryview)，从本质上讲，它们是C级库公开用于python的内存块的一种方式.基本上，这是一个用于对原始内存进行托管访问的python接口.

If you're not familiar with buffer objects (memoryview in python3.x), they're essentially a way for C-level libraries to expose a block of memory for use in python. It's basically a python interface for managed access to raw memory.

如果您正在使用暴露缓冲区接口的工具，那么您可能要使用frombuffer. (Python 2.x字符串和python 3.x bytes公开了缓冲区接口，但是您将获得一个只读数组，因为python字符串是不可变的.)

If you were working with something that exposed the buffer interface, then you'd probably want to use frombuffer. (Python 2.x strings and python 3.x bytes expose the buffer interface, but you'll get a read-only array, as python strings are immutable.)

否则，请使用fromstring从字符串创建一个numpy数组. (除非您知道自己在做什么，并且想严格控制内存使用情况，等等.)

Otherwise, use fromstring to create a numpy array from a string. (Unless you know what you're doing, and want to tightly control memory use, etc.)

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