Numpy float64 与 Python 浮点数
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Numpy float64 vs Python float
提问by mchangun
I'm battling some floating point problems in Pandas read_csv function. In my investigation, I found this:
我正在与 Pandas read_csv 函数中的一些浮点问题作斗争。在我的调查中，我发现了这一点：
In [15]: a = 5.9975
In [16]: a
Out[16]: 5.9975
In [17]: np.float64(a)
Out[17]: 5.9974999999999996
Why is builtin floatof Python and the np.float64type from Python giving different results? I thought they were both C++ doubles?
为什么内置float的 Python 和np.float64来自 Python的类型给出不同的结果？我以为他们都是C++双打？
采纳答案by Ignacio Vazquez-Abrams
>>> numpy.float64(5.9975).hex()
'0x1.7fd70a3d70a3dp+2'
>>> (5.9975).hex()
'0x1.7fd70a3d70a3dp+2'
They are the same number. What differs is their representation; the Python native type uses a "sane" representation, and the NumPy type uses an accurate representation.
他们是同一个数字。不同的是他们的代表；Python 本机类型使用“理智”表示，而 NumPy 类型使用准确表示。
