numpy.maximum

numpy.fmax

# numpy.minimum¶

`numpy.``minimum`(x1, x2[, out]) = <ufunc 'minimum'>

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

`maximum`
Element-wise maximum of two arrays, propagates NaNs.
`fmin`
Element-wise minimum of two arrays, ignores NaNs.
`amin`
The minimum value of an array along a given axis, propagates NaNs.
`nanmin`
The minimum value of an array along a given axis, ignores 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])
>>> np.minimum(-np.Inf, 1)
-inf
```