NumPy proposes a way to get the index of the maximum value of an array via np.argmax
.
I would like a similar thing, but returning the indexes of the N
maximum values.
For instance, if I have an array, [1, 3, 2, 4, 5]
, function(array, n=3)
would return the indices [4, 3, 1]
which correspond to the elements [5, 4, 3]
.
index() functions to find out the index of the maximum value in a list. Use the enumerate() function to find out the index of the maximum value in a list. Use the numpy. argmax() function of the NumPy library to find out the index of the maximum value in a list.
Indexing can be done in numpy by using an array as an index. In case of slice, a view or shallow copy of the array is returned but in index array a copy of the original array is returned. Numpy arrays can be indexed with other arrays or any other sequence with the exception of tuples.
Newer NumPy versions (1.8 and up) have a function called argpartition
for this. To get the indices of the four largest elements, do
>>> a = np.array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0]) >>> a array([9, 4, 4, 3, 3, 9, 0, 4, 6, 0]) >>> ind = np.argpartition(a, -4)[-4:] >>> ind array([1, 5, 8, 0]) >>> top4 = a[ind] >>> top4 array([4, 9, 6, 9])
Unlike argsort
, this function runs in linear time in the worst case, but the returned indices are not sorted, as can be seen from the result of evaluating a[ind]
. If you need that too, sort them afterwards:
>>> ind[np.argsort(a[ind])] array([1, 8, 5, 0])
To get the top-k elements in sorted order in this way takes O(n + k log k) time.
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