How to round a numpy array?

If you want the output to be

array([1.6e-01, 9.9e-01, 3.6e-04])

the problem is not really a missing feature of NumPy, but rather that this sort of rounding is not a standard thing to do. You can make your own rounding function which achieves this like so:

def my_round(value, N):
    exponent = np.ceil(np.log10(value))
    return 10**exponent*np.round(value*10**(-exponent), N)

For a general solution handling 0 and negative values as well, you can do something like this:

def my_round(value, N):
    value = np.asarray(value).copy()
    zero_mask = (value == 0)
    value[zero_mask] = 1.0
    sign_mask = (value < 0)
    value[sign_mask] *= -1
    exponent = np.ceil(np.log10(value))
    result = 10**exponent*np.round(value*10**(-exponent), N)
    result[sign_mask] *= -1
    result[zero_mask] = 0.0
    return result

Numpy provides two identical methods to do this. Either use

np.round(data, 2)

or

np.around(data, 2)

as they are equivalent.

See the documentation for more information.

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Examples:

>>> import numpy as np
>>> a = np.array([0.015, 0.235, 0.112])
>>> np.round(a, 2)
array([0.02, 0.24, 0.11])
>>> np.around(a, 2)
array([0.02, 0.24, 0.11])
>>> np.round(a, 1)
array([0. , 0.2, 0.1])