numpy: what is the logic of the argmin() and argmax() functions?

argmax, arrays, numpy, python

Solution

By adding the `axis` argument, NumPy looks at the rows and columns individually. When it's not given, the array `a` is flattened into a single 1D array.

`axis=0` means that the operation is performed down the columns of a 2D array `a` in turn.

For example `np.argmin(a, axis=0)` returns the index of the minimum value in each of the four columns. The minimum value in each column is shown in bold below:

>>> a
array([[ 1,  2,  4,  7],  # 0
       [ 9, 88,  6, 45],  # 1
       [ 9, 76,  3,  4]]) # 2

>>> np.argmin(a, axis=0)
array([0, 0, 2, 2])

On the other hand, `axis=1` means that the operation is performed across the rows of `a`.

That means `np.argmin(a, axis=1)` returns `[0, 2, 2]` because `a` has three rows. The index of the minimum value in the first row is 0, the index of the minimum value of the second and third rows is 2:

>>> a
#        0   1   2   3
array([[ 1,  2,  4,  7],
       [ 9, 88,  6, 45],
       [ 9, 76,  3,  4]])

>>> np.argmin(a, axis=1)
array([0, 2, 2])

Problem

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