Sorting a 2D numpy array by multiple axes

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]]