Numpy matrix.argmax () method, we can find the index value of the smallest element in a given matrix that has one or more dimensions.
Return: Return index number of minimum elements in matrix
Example # 1:
In this example, we see that using the
matrix.argmax () we can find the index of the minimum element in the given matrix.
Example # 2:
import numpy as np def find_nearest(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] array = np.random.random(10) print(array) # [ 0.21069679 0.61290182 0.63425412 0.84635244 0.91599191 0.00213826 # 0.17104965 0.56874386 0.57319379 0.28719469] value = 0.5 print(find_nearest(array, value)) # 0.568743859261
Say that you have a list
values = [3,6,1,5], and need the index of the smallest element, i.e.
index_min = 2 in this case.
Avoid the solution with
itemgetter() presented in the other answers, and use instead
index_min = min(range(len(values)), key=values.__getitem__)
because it doesn"t require to
import operator nor to use
enumerate, and it is always faster(benchmark below) than a solution using
If you are dealing with numpy arrays or can afford
numpy as a dependency, consider also using
import numpy as np index_min = np.argmin(values)
This will be faster than the first solution even if you apply it to a pure Python list if:
I have run the benchmark on my machine with python 2.7 for the two solutions above (blue: pure python, first solution) (red, numpy solution) and for the standard solution based on
itemgetter() (black, reference solution).
The same benchmark with python 3.5 showed that the methods compare exactly the same of the python 2.7 case presented above
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