Better rounding in Python's NumPy.around: Rounding NumPy Arrays

Question

I am looking for a way to round a numpy array in a more intuitive fashion. I have some of several floats, and would like to limit them to only a few decimal places. This would be done as such:

>>>import numpy as np
>>>np.around([1.21,5.77,3.43], decimals=1)
array([1.2, 5.8, 3.4])

Now the problem arises when trying to round numbers that are exactly between the rounding steps. I would like 0.05 rounded to 0.1, but np.around is set to round to the "nearest even number". This produces the following:

>>>np.around([0.55, 0.65, 0.05], decimals=1)
array([0.6, 0.6, 0.0])

My question then amounts to, what is the most effective way to round to the nearest number, and not simply the nearest even number.

For more info on np.around, see its documentation.

Answer 1

The way around does this is correct, but if you want to do something different, you could, for example, subtract an amount much less than the rounding precision, for example,

def myround(a, decimals=1):
     return np.around(a-10**(-(decimals+5)), decimals=decimals)

In [22]: myround(np.array([ 1.21,  5.77,  3.43]), 1)
Out[22]: array([ 1.2,  5.8,  3.4])

In [23]: myround(np.array([ 0.55,  0.65,  0.05]), 1)
Out[23]: array([ 0.5,  0.6,  0. ])

The reason I chose 5 here, was that by not including the even/odd distinction, you're implicitely introducing an average error of about 10**(-(decimal+1))/2 so you shouldn't complain about an explicit error of 1/10000th of that error.