If I want to find the minimum value in each column of a NumPy array, I can use the numpy.amin() function. However, is there a way to find the two minimum values in each column, that is faster than sorting each column?
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2 Answers
You can simply use np.partition along the columns to get smallest N numbers -
N = 2
np.partition(a,kth=N-1,axis=0)[:N]
This doesn't actually sort the entire data, simply partitions into two sections such that smallest N numbers are in the first section, also called as partial-sort.
Bonus (Getting top N elements) : Similarly, to get the top N numbers per col, simply use negative kth value -
np.partition(a,kth=-N,axis=0)[-N:]
Along other axes and higher dim arrays
To use it along other axes, change the axis value. So, along rows, it would be axis=1 for a 2D array and extend the same way for higher dimension ndarrays.
Comments
Use the min() method, and specify the axis you want to average over:
a = np.random.rand(10,3)
a.min(axis=0)
gives:
array([0.04435587, 0.00163139, 0.06327353])
a.min(axis=1)
gives
array([0.01354386, 0.08996586, 0.19332211, 0.00163139, 0.55650945,
0.08409907, 0.23015718, 0.31463493, 0.49117553, 0.53646868])
2 Comments
Joe
The problem was, I wanted the two smallest values per column, not just the minimum of each column. I didn't want to sort each column, since that seemed too computationally expensive. I wasn't aware of NumPy's partition function, which does exactly what I need.
Richard
Ah ok - gotcha!