numpy.minimum

numpy.minimum(x1, x2[, out])

Element-wise minimum of array elements.

Compare two arrays and returns a new array containing the element-wise minima. 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.

Parameters:

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.

Returns:

y : {ndarray, scalar}

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

See also

maximum

element-wise minimum that propagates nans.

fmax

element-wise maximum that ignores nans unless both inputs are nans.

fmin

element-wise minimum that ignores nans unless both inputs are nans.

Notes

The minimum 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.minimum([2, 3, 4], [1, 5, 2])
array([1, 3, 2])

>>> np.minimum(np.eye(2), [0.5, 2]) # broadcasting
array([[ 0.5,  0. ],
       [ 0. ,  1. ]])

>>> np.minimum([np.nan, 0, np.nan],[0, np.nan, np.nan])
array([ NaN,  NaN,  NaN])