bundles / numpy latest / numpy / maximum
ufunc
numpy:maximum
source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/__init__.py
Summary
Element-wise maximum of array elements.
Extended Summary
Compare two arrays and return 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.
Parameters
x1, x2: array_likeThe arrays holding the elements to be compared. If
x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).out: ndarray, None, or tuple of ndarray and None, optionalA location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where: array_like, optionalThis condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None, locations within it where the condition is False will remain uninitialized.**kwargsFor other keyword-only arguments, see the
ufunc docs <ufuncs.kwargs>.
Returns
y: ndarray or scalarThe maximum of x1 and x2, element-wise. This is a scalar if both x1 and x2 are scalars.
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
import numpy as np np.maximum([2, 3, 4], [1, 5, 2])✓
np.maximum(np.eye(2), [0.5, 2]) # broadcasting
✗np.maximum([np.nan, 0, np.nan], [0, np.nan, np.nan])
✓np.maximum(np.inf, 1)
✗See also
- amax
The maximum value of an array along a given axis, propagates NaNs.
- amin
- fmax
Element-wise maximum of two arrays, ignores NaNs.
- fmin
- minimum
Element-wise minimum of two arrays, propagates NaNs.
- nanmax
The maximum value of an array along a given axis, ignores NaNs.
- nanmin
Aliases
-
numpy.maximum