Question

I’m running into an issue with python automatically rounding very small numbers (smaller than 1e-8) when subtracting an array from an single float. Take this example:

 import numpy as np
 float(1) - np.array([1e-10, 1e-5])

Any thoughts on how to force python not to round? This is forcing me to divide by zero in some cases, and becoming a problem. The same problem arises when subtracting from an numpy array.

Answer 1

Mostly, it’s just the repr of numpy arrays that’s fooling you.

Consider your example above:

import numpy as np  
x = float(1) - np.array([1e-10, 1e-5]) 
print x
print x[0]
print x[0] == 1.0

This yields:

[ 1.      0.99999 ]
0.99999999999
False

So the first element isn’t actually zero, it’s just the pretty-printing of numpy arrays that’s showing it that way.

This can be controlled by numpy.set_printoptions.

Of course, numpy is fundementally using limited precision floats. The whole point of numpy is to be a memory-efficient container for arrays of similar data, so there’s no equivalent of the decimal class in numpy.

However, 64-bit floats have a decent range of precision. You won’t hit too many problems with 1e-10 and 1e-5. If you need, there’s also a numpy.float128 dtype, but operations will be much slower than using native floats.