numpy数组中有多余的小数? [英] extra decimals in numpy array?

问题描述

假设我有一个浮点列表和一个要压缩的numpy数组:

Let's say I have one list of floats and a numpy array that I wish to zip:

>>> import numpy as np
>>> n1 = [0.9, 1.1]
>>> n2 = np.array([0.9,1.1])
>>> zip(n1,n2)
    [(0.9, 0.90000000000000002), (1.1, 1.1000000000000001)]

为什么我会得到所有这些小数?有没有一种方法可以使numpy浮点数的行为像python浮点数一样?

Why am I getting all those decimals? Is there a way to make the numpy floats behave like python floats?

似乎计算也受numpy存储浮动方式的影响:

It seems that calculations are also affected by the way numpy stores floats:

>>> nb1 = n2[1]+0.1
>>> nb1
    1.2000000000000002
>>> nb2 = nb1 - 1.2 #nb2 should be equal to 0
    2.2204460492503131e-16
>>> nb3 = np.asscalar(nb2)
>>> nb3
    2.2204460492503131e-16 #nb3 should be equal to 0
>>> type(nb3)
    float

推荐答案

python浮点数和numpy数组都可能是64位浮点数，但它们被封装"在不同的对象中.差异可能是由于python浮点数和numpy浮点数的repr不同.

Both the python floats and the numpy arrays are probably 64-bit floating point numbers, but they are 'encapsulated' in different objects. The difference is probably due to different repr for python floats and numpy floats.

In [12]: n1[0]
Out[12]: 0.9

In [13]: n2[0]
Out[13]: 0.90000000000000002

In [14]: n1[0] == n2[0]
Out[14]: True

In [15]: type(n1[0])
Out[15]: float

In [16]: type(n2[0])
Out[16]: numpy.float64

同一问题可能触发了上一个问题.我个人认为这是numpy中的错误.

The same problem probably triggered a previous question. Personally, I consider this a bug in numpy.

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