np.max is just an alias for np.amax. This function only works on a single input array and finds the value of maximum element in that entire array (returning a scalar). Alternatively, it takes an axis argument and will find the maximum value along an axis of the input array (returning a new array).
>>> a = np.array([[0, 1, 6],
[2, 4, 1]])
>>> np.max(a)
6
>>> np.max(a, axis=0) # max of each column
array([2, 4, 6])
The default behaviour of np.maximum is to take two arrays and compute their element-wise maximum. Here, 'compatible' means that one array can be broadcast to the other. For example:
>>> b = np.array([3, 6, 1])
>>> c = np.array([4, 2, 9])
>>> np.maximum(b, c)
array([4, 6, 9])
But np.maximum is also a universal function which means that it has other features and methods which come in useful when working with multidimensional arrays. For example you can compute the cumulative maximum over an array (or a particular axis of the array):
>>> d = np.array([2, 0, 3, -4, -2, 7, 9])
>>> np.maximum.accumulate(d)
array([2, 2, 3, 3, 3, 7, 9])
This is not possible with np.max.
You can make np.maximum imitate np.max to a certain extent when using np.maximum.reduce:
>>> np.maximum.reduce(d)
9
>>> np.max(d)
9
Basic testing suggests the two approaches are comparable in performance; and they should be, as np.max() actually calls np.maximum.reduce to do the computation.
np.max is just an alias for np.amax. This function only works on a single input array and finds the value of maximum element in that entire array (returning a scalar). Alternatively, it takes an axis argument and will find the maximum value along an axis of the input array (returning a new array).
>>> a = np.array([[0, 1, 6],
[2, 4, 1]])
>>> np.max(a)
6
>>> np.max(a, axis=0) # max of each column
array([2, 4, 6])
The default behaviour of np.maximum is to take two arrays and compute their element-wise maximum. Here, 'compatible' means that one array can be broadcast to the other. For example:
>>> b = np.array([3, 6, 1])
>>> c = np.array([4, 2, 9])
>>> np.maximum(b, c)
array([4, 6, 9])
But np.maximum is also a universal function which means that it has other features and methods which come in useful when working with multidimensional arrays. For example you can compute the cumulative maximum over an array (or a particular axis of the array):
>>> d = np.array([2, 0, 3, -4, -2, 7, 9])
>>> np.maximum.accumulate(d)
array([2, 2, 3, 3, 3, 7, 9])
This is not possible with np.max.
You can make np.maximum imitate np.max to a certain extent when using np.maximum.reduce:
>>> np.maximum.reduce(d)
9
>>> np.max(d)
9
Basic testing suggests the two approaches are comparable in performance; and they should be, as np.max() actually calls np.maximum.reduce to do the computation.
You've already stated why np.maximum is different - it returns an array that is the element-wise maximum between two arrays.
As for np.amax and np.max: they both call the same function - np.max is just an alias for np.amax, and they compute the maximum of all elements in an array, or along an axis of an array.
In [1]: import numpy as np
In [2]: np.amax
Out[2]: <function numpy.core.fromnumeric.amax>
In [3]: np.max
Out[3]: <function numpy.core.fromnumeric.amax>
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Well from my timings it follows if you already have numpy array a you should use a.max (the source tells it's the same as np.max if a.max available). But if you have built-in list then most of the time takes converting it into np.ndarray => that's why max is better in your timings.
In essense: if np.ndarray then a.max, if list and no need for all the machinery of np.ndarray then standard max.
I was also interested in this and tested the three variants with perfplot (a little project of mine). Result: You're not going wrong with a.max().
Code to reproduce the plot:
import numpy as np
import perfplot
b = perfplot.bench(
setup=np.random.rand,
kernels=[max, np.max, lambda a: a.max()],
labels=["max(a)", "np.max(a)", "a.max()"],
n_range=[2 ** k for k in range(25)],
xlabel="len(a)",
)
b.show()