Numpy float64 vs Python float
Question:
I’m battling some floating point problems in Pandas read_csv function. In my investigation, I found this:
In [15]: a = 5.9975
In [16]: a
Out[16]: 5.9975
In [17]: np.float64(a)
Out[17]: 5.9974999999999996
Why is builtin float of Python and the np.float64 type from Python giving different results? I thought they were both C++ doubles?
Answers:
>>> 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.
I’m battling some floating point problems in Pandas read_csv function. In my investigation, I found this:
In [15]: a = 5.9975
In [16]: a
Out[16]: 5.9975
In [17]: np.float64(a)
Out[17]: 5.9974999999999996
Why is builtin float of Python and the np.float64 type from Python giving different results? I thought they were both C++ doubles?
>>> 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.