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How to round the values in a NumPy array to the nearest integer.

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This tutorial imports Numpy.

In [1]:
import numpy as np

Round and ARound

np.round() and np.around() are equivalent methods but np.around() is prefered. These methods round all values in a NumPy array according to the standard and accepted rules for rounding in scientific: for a number of the form X.5, round it up to (X+1).0 if X is odd and round down to X.0 when X is even. This is equivalent to saying:

"Round each number of the form X.5 to the nearest even number."

In [2]:
array = np.array([0.1, 2.9, 7.5, 8.5])

array([ 0.,  3.,  8.,  8.])
In [3]:
Help on function around in module numpy.core.fromnumeric:

around(a, decimals=0, out=None)
    Evenly round to the given number of decimals.
    a : array_like
        Input data.
    decimals : int, optional
        Number of decimal places to round to (default: 0).  If
        decimals is negative, it specifies the number of positions to
        the left of the decimal point.
    out : ndarray, optional
        Alternative output array in which to place the result. It must have
        the same shape as the expected output, but the type of the output
        values will be cast if necessary. See `doc.ufuncs` (Section
        "Output arguments") for details.
    rounded_array : ndarray
        An array of the same type as `a`, containing the rounded values.
        Unless `out` was specified, a new array is created.  A reference to
        the result is returned.
        The real and imaginary parts of complex numbers are rounded
        separately.  The result of rounding a float is a float.
    See Also
    ndarray.round : equivalent method
    ceil, fix, floor, rint, trunc
    For values exactly halfway between rounded decimal values, Numpy
    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
    -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
    to the inexact representation of decimal fractions in the IEEE
    floating point standard [1]_ and errors introduced when scaling
    by powers of ten.
    .. [1] "Lecture Notes on the Status of  IEEE 754", William Kahan,
    .. [2] "How Futile are Mindless Assessments of
           Roundoff in Floating-Point Computation?", William Kahan,
    >>> np.around([0.37, 1.64])
    array([ 0.,  2.])
    >>> np.around([0.37, 1.64], decimals=1)
    array([ 0.4,  1.6])
    >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
    array([ 0.,  2.,  2.,  4.,  4.])
    >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
    array([ 1,  2,  3, 11])
    >>> np.around([1,2,3,11], decimals=-1)
    array([ 0,  0,  0, 10])

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