perimosocordiae - 7 months ago 17

Python Question

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]]
```

Source (Stackoverflow)

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