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Showing content from http://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.maximum.html below:

numpy.maximum — NumPy v1.11 Manual

Element-wise maximum of array elements.

Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

x1, x2 : array_like

The arrays holding the elements to be compared. They must have the same shape, or shapes that can be broadcast to a single shape.

y : ndarray or scalar

The maximum of x1 and x2, element-wise. Returns scalar if both x1 and x2 are scalars.

See also

minimum

Element-wise minimum of two arrays, propagates NaNs.

fmax

Element-wise maximum of two arrays, ignores NaNs.

amax

The maximum value of an array along a given axis, propagates NaNs.

nanmax

The maximum value of an array along a given axis, ignores NaNs.

fmin, amin, nanmin

Notes

The maximum is equivalent to np.where(x1 >= x2, x1, x2) when neither x1 nor x2 are nans, but it is faster and does proper broadcasting.

Examples

>>> np.maximum([2, 3, 4], [1, 5, 2])
array([2, 5, 4])

>>> np.maximum(np.eye(2), [0.5, 2]) # broadcasting
array([[ 1. ,  2. ],
       [ 0.5,  2. ]])

>>> np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
array([ NaN,  NaN,  NaN])
>>> np.maximum(np.Inf, 1)
inf

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