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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 
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Sorting 2D Numpy Array by column at index 1 Select the column at index 1 from 2D numpy array i.e. It returns the values at 2nd column i.e. column at index position 1 i.e. Now get the array of indices that sort this column i.e. It returns the index positions that can sort the above column i.e.
NumPy arrays can be sorted by a single column, row, or by multiple columns or rows using the argsort() function. The argsort function returns a list of indices that will sort the values in an array in ascending value.
Sort the rows of a 2D array in descending order The code axis = 1 indicates that we'll be sorting the data in the axis-1 direction, and by using the negative sign in front of the array name and the function name, the code will sort the rows in descending order.
Rank of the array is the number of singular values of the array that are greater than tol. Input vector or stack of matrices. Threshold below which SVD values are considered zero. If tol is None, and S is an array with singular values for M, and eps is the epsilon value for datatype of S , then tol is set to S.
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]] If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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