Find unique rows in numpy.array

Question

I need to find unique rows in a numpy.array.

For example:

>>> a # I have
array([[1, 1, 1, 0, 0, 0],
       [0, 1, 1, 1, 0, 0],
         


        

Answer 1

We can actually turn m x n numeric numpy array into m x 1 numpy string array, please try using the following function, it provides count, inverse_idx and etc, just like numpy.unique:

import numpy as np

def uniqueRow(a):
    #This function turn m x n numpy array into m x 1 numpy array storing 
    #string, and so the np.unique can be used

    #Input: an m x n numpy array (a)
    #Output unique m' x n numpy array (unique), inverse_indx, and counts 

    s = np.chararray((a.shape[0],1))
    s[:] = '-'

    b = (a).astype(np.str)

    s2 = np.expand_dims(b[:,0],axis=1) + s + np.expand_dims(b[:,1],axis=1)

    n = a.shape[1] - 2    

    for i in range(0,n):
         s2 = s2 + s + np.expand_dims(b[:,i+2],axis=1)

    s3, idx, inv_, c = np.unique(s2,return_index = True,  return_inverse = True, return_counts = True)

    return a[idx], inv_, c

Example:

A = np.array([[ 3.17   9.502  3.291],
  [ 9.984  2.773  6.852],
  [ 1.172  8.885  4.258],
  [ 9.73   7.518  3.227],
  [ 8.113  9.563  9.117],
  [ 9.984  2.773  6.852],
  [ 9.73   7.518  3.227]])

B, inv_, c = uniqueRow(A)

Results:

B:
[[ 1.172  8.885  4.258]
[ 3.17   9.502  3.291]
[ 8.113  9.563  9.117]
[ 9.73   7.518  3.227]
[ 9.984  2.773  6.852]]

inv_:
[3 4 1 0 2 4 0]

c:
[2 1 1 1 2]