That documentation makes more sense when you think about multidimensional arrays.
>>> x = numpy.array([[0, 1], ... [3, 2]]) >>> x.argmin(axis=0) array([0, 0]) >>> x.argmin(axis=1) array([0, 1])
With an axis specified,
argmin takes one-dimensional subarrays along the given axis and returns the first index of each subarray’s minimum value. It doesn’t return all indices of a single minimum value.
To get all indices of the minimum value, you could do
numpy.where(x == x.min())