a, b, c = np.array([10]), np.array([2]), np.array([7]) min_val = np.minimum(a, b, c) min_val array([2]) max_val = np.maximum(a, b, c) max_val array([10]) min_val array([10])
(I'm using numpy 1.4, and I observed the same behavior with numpy 2.0.0.dev8600 on another machine). I'm quite surprised by this behavior (It took me quite a long time to figure out what was happening in a script of mine that wasn't giving what I expected, because of np.maximum changing the output of np.minimum). Is it a bug, or am I missing something?
Read the documentation for numpy.minimum and numpy.maximum: they give you element-wise minimum values from two arrays passed as arguments. E.g.:
numpy.minimum([1,2,3],[3,2,1]) array([1, 2, 1])
The optional third parameter to numpy.minimum is an "out" array - an array to place the results into instead of making a new array for that purpose. (This can save time / memory in various cases.) This should therefore be enough to explain the above behavior. (That is, min_val and max_val wind up being just other names for the array 'c', which gets modified in-place by the numpy.minimum and numpy.maximum.) If you want the minimum value of a sequence of arbitrary length, use the python min() function. If you have a numpy array already and you want the minimum (global, or along a particular axis), use the min() method of the array, or numpy.min(arr). Zach