Marjolaine, Solution: unique_index = [i for i,x in enumerate(l) if not or x != l[i-1]]
Remember that enumerate gives the index,value pairs of the items in any iterable object. Try it for yourself. Here's the output from my IDLE session. In [1]: l = [(1,1), (2,3), (1, 1), (4,5), (2,3), (10,21)] In [2]: l.sort() In [3]: l Out[3]: [(1, 1), (1, 1), (2, 3), (2, 3), (4, 5), (10, 21)] In [4]: unique_index = [i for i, x in enumerate(l) if not i or x != l[i-1]] In [5]: unique_index Out[5]: [0, 2, 4, 5] BTW, I'm posting my response to the numpy-discussion group so others may benefit. It's best to address your questions to the group, as individuals are not always available to answer your question in a timely manner. And by posting your message to the group, you draw from a large body of very knowledgeable people who will gladly help you. Daran -- > I saw your message on the numpy discussion board regarding the solution for > the efficient removal of duplicates. I have the same problem but need to need to return > the indices of the values as an input with associated z values. > I was wondering if there was any ways to have the method you propoosed return > the indices of the duplicate (or alternatively unique) values in a. > > > Here is the piece of code that you suggested at the time (Re: > [Numpy-discussion] Efficient removal of duplicates, posted on Tue, 16 Dec 2008 01:10:00 -0800) > > > --------------------------------------------- > import numpy as np > > a = [(x0,y0), (x1,y1), ...] # A numpy array, but could be a list l = > a.tolist() > l.sort() > unique = [x for i, x in enumerate(l) if not i or x != l[i-1]] # <---- > a_unique = np.asarray(unique) > > --------------------------------------------- > > Best regards, marjolaine > > > > > -- > This message is subject to the CSIR's copyright terms and conditions, e-mail > legal notice, and implemented Open Document Format (ODF) standard. > The full disclaimer details can be found at > http://www.csir.co.za/disclaimer.html. > > This message has been scanned for viruses and dangerous content by > MailScanner, and is believed to be clean. MailScanner thanks Transtec Computers for their support. > > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
