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I have a numpy array of arrays x = [[1, 3, 4, 5], [6, 2, 5, 7]]. I want to get N maximum values from each array of the numpy array: [[5, 4], [7, 6]]. I have tried using np.argpartition(x, -N, axis=0)[-N:] but it gives ValueError: kth(=-3) out of bounds (1). What is the efficient way for doing this?

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    How is [[1,3],[2,5]] "N maximum values from each array"? And I'd be more concerned with doing it *correctly" than "efficiently". Commented Apr 1, 2022 at 12:23
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    It's kind of ambiguous what the expected result is, does np.partition(x, -N, axis=1)[:,-N:] give the desired result? Commented Apr 1, 2022 at 12:30
  • @ScottHunter sorry it was my mistake, I have edited it. Commented Apr 1, 2022 at 14:05
  • You got the wrong axis. Commented Apr 1, 2022 at 16:36
  • axis=1 just gives the whole array Commented Apr 1, 2022 at 18:30

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You can do this by sorting each row and slicing as you want:

np.sort(x, axis=1)[:, :2]  # --> [[1 3] [2 5]]    2 minimum in each row
np.sort(x, axis=1)[:, 2:]  # --> [[4 5] [6 7]]    2 maximum in each row
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2 Comments

This solution works but partition is O(n), sort is O(n * log(n)).
@MichaelSzczesny, I agree. I saw partition is corrected by your comment; So, I didn't write that in my answer. Also, the author didn't emphasize on using just partition to solve the problem. Again, partition is the better choice.

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