How does numpy argmax work?

Question

So I know that the numpy argmax retrieves the maximum value along an axis. Thus,

x = np.array([[12,11,10,9],[16,15,14,13],[20,19,18,17]]) print(x) print(x.sum(axis=1)) print(x.sum(axis=0))

would output,

[[12 11 10 9] [16 15 14 13] [20 19 18 17]] [42 58 74] [48 45 42 39]

This makes sense as the sum along axis 1 (row) is [42 58 74] and axis 0 (column) is [48 45 42 39]. However, i am confused of how argmax work. From my understanding, argmax is supposed to return the max number along the axis. Below is my code and output.

Code: print(np.argmax(x,axis=1)). Output: [0 0 0]

Code: print(np.argmax(x,axis=0)). Output: [2 2 2 2]

Where does 0 and 2 come from? I've deliberately used a set of more complex integer values (9..20) so as to distinguish between the 0 and 2 and the integer values inside the array.

i googled for it and yet i dont understand. I even tried coding it out to understand how it works. Do u think i will go through the trouble of posting a long post and trying it out without googling? — user859385
– user859385, Commented May 22, 2018 at 6:21
np.max returns the maximum values along the respective axis; np.argmax returns 'where' those values occur, the index. — hpaulj
– hpaulj, Commented May 22, 2018 at 6:26

llllllllll · Accepted Answer · 2018-05-22 06:26:43Z

np.argmax(x,axis=1) returns the index of maximum of in every row.

axis=1 means "along axis 1", i.e, row.

[[12 11 10 9] <-- max at index 0 [16 15 14 13] <-- max at index 0 [20 19 18 17]] <-- max at index 0

Thus its output is [0 0 0].

It's similar for np.argmax(x,axis=0), but now it returns the index of maximum of in every column.

Thank you for the response. It's good to see people replying in kind unlike how the OP was initially down-voted and rebuked by another user.

Kevin · Accepted Answer · 2019-06-10 18:45:54Z

Correction: axis=0 refers to rows, not to columns. axis=1 refers to columns, not to rows.

x = np.array([[12,11,10,9],[16,15,14,13],[20,19,18,17]]) print(x) [[12 11 10 9] [16 15 14 13] [20 19 18 17]] np.argmax(x, axis=0) array([2, 2, 2, 2] # third row, index 2 of each of the 4 columns np.argmax(x, axis=1) array([0, 0, 0] # first column, index 0 of each of the three rows.

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