numpy.argmin (array, axis = None, out = None): returns the indices of the minimum element in an array on a specific axis.
Parameters:
array: Input array to work on axis: [int, optional] Along a specified axis like 0 or 1 out: [array optional] Provides a feature to insert output to the out array and it should be of appropriate shape and dtype
Return:
Array of indices into the array with same shape as array.shape with the dimension along axis removed.
Code 1 :

Output:
INPUT ARRAY: [0 1 2 3 4 5 6 7] Indices of min element: 0
Code 2:

Output:
INPUT ARRAY: [[8 13 5 0] [0 2 5 3] [10 7 15 15] [3 11 4 12]] Indices of min element: [1 1 3 0]
Code 3:
Output:
array: [[0 1 2 3 4] [5 6 7 8 9]] array: [[10 1 2 3 4] [5 1 7 8 9]] array: 1 min ELEMENT INDICES: [1 0 0 0 0]
Links:
argmin.html # numpy.argmin> https://docs.scipy.org/doc/numpydev/reference/generated/numpy.argmin.html#numpy.argmin
Notes:
These codes will not work for online IDs. Please run them on your systems to see how they work
This article is provided by Mohit Gupta_OMG
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 itemgetter()
.
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:
numpy
arrayI 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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