Slicing and getting the max value with Numpy Python

Question

Im trying to write a code with numpy where it outputs the maximum value between indexes. I think using argmax could be usable. However I do not know how I can use slices without using a for loop in python. If there is a pandas function for this it could be useable too. I want to make the computation as fast as possible.

list_ = np.array([9887.89, 9902.99, 9902.99, 9910.23, 9920.79, 9911.34, 9920.01, 9927.51, 9932.3, 9932.33, 9928.87, 9929.22, 9929.22, 9935.24, 9935.24, 9935.26, 9935.26, 9935.68, 9935.68, 9940.5])
indexes = np.array([0, 5, 10, 19])

Expected result:

Max number between index(0 - 5):  9920.79 at index 5
Max number between index(5 - 10): 9932.33 at index 10
Max number between index(10 - 19): 9940.5 at index 19

question from:https://stackoverflow.com/questions/65931080/slicing-and-getting-the-max-value-with-numpy-python

Answer 1

You can use reduceat directly yo your array without the need to splice/split it:

np.maximum.reduceat(list_,indexes[:-1])

output:

array([9932.33, 9929.22, 9940.5 ])