perimosocordiae perimosocordiae - 7 months ago 17
Python Question

Sorting a 2D numpy array by multiple axes

I have a 2D numpy array of shape (N,2) which is holding N points (x and y coordinates). For example:

array([[3, 2],
[6, 2],
[3, 6],
[3, 4],
[5, 3]])


I'd like to sort it such that my points are ordered by x-coordinate, and then by y in cases where the x coordinate is the same. So the array above should look like this:

array([[3, 2],
[3, 4],
[3, 6],
[5, 3],
[6, 2]])


If this was a normal Python list, I would simply define a comparator to do what I want, but as far as I can tell, numpy's sort function doesn't accept user-defined comparators. Any ideas?




EDIT: Thanks for the ideas! I set up a quick test case with 1000000 random integer points, and benchmarked the ones that I could run (sorry, can't upgrade numpy at the moment).

Mine: 4.078 secs
mtrw: 7.046 secs
unutbu: 0.453 secs

Answer

Using lexsort:

import numpy as np    
a = np.array([(3, 2), (6, 2), (3, 6), (3, 4), (5, 3)])

ind = np.lexsort((a[:,1],a[:,0]))    

a[ind]
# array([[3, 2],
#       [3, 4],
#       [3, 6],
#       [5, 3],
#       [6, 2]])

a.ravel() returns a view if a is C_CONTIGUOUS. If that is true, @ars's method, slightly modifed by using ravel instead of flatten, yields a nice way to sort a in-place:

a = np.array([(3, 2), (6, 2), (3, 6), (3, 4), (5, 3)])
dt = [('col1', a.dtype),('col2', a.dtype)]
assert a.flags['C_CONTIGUOUS']
b = a.ravel().view(dt)
b.sort(order=['col1','col2'])

Since b is a view of a, sorting b sorts a as well:

print(a)
# [[3 2]
#  [3 4]
#  [3 6]
#  [5 3]
#  [6 2]]
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