utils.data: Use list comprehension for uniq_stable()

This should be faster than and explicit for loop.  Also, a set makes
more sense than a dict if we only care about values.  After these
changes, the resulting code is the same as Dave Kirby's uniqify()
suggestion on Peter Bengtsson's blog [1].

[1]: http://www.peterbe.com/plog/uniqifiers-benchmark

IPython/utils/data.py

@@ -1,106 +1,96
 # encoding: utf-8
 """Utilities for working with data structures like lists, dicts and tuples.
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 import types
 
 #-----------------------------------------------------------------------------
 # Code
 #-----------------------------------------------------------------------------
 
 def uniq_stable(elems):
     """uniq_stable(elems) -> list
 
     Return from an iterable, a list of all the unique elements in the input,
     but maintaining the order in which they first appear.
 
-    A naive solution to this problem which just makes a dictionary with the
-    elements as keys fails to respect the stability condition, since
-    dictionaries are unsorted by nature.
-
-    Note: All elements in the input must be valid dictionary keys for this
-    routine to work, as it internally uses a dictionary for efficiency
-    reasons."""
-
-    unique = []
-    unique_dict = {}
-    for nn in elems:
-        if nn not in unique_dict:
-            unique.append(nn)
-            unique_dict[nn] = None
-    return unique
+    Note: All elements in the input must be hashable for this routine
+    to work, as it internally uses a set for efficiency reasons.
+    """
+    seen = set()
+    return [x for x in elems if x not in seen and not seen.add(x)]
 
 
 def sort_compare(lst1, lst2, inplace=1):
     """Sort and compare two lists.
 
     By default it does it in place, thus modifying the lists. Use inplace = 0
     to avoid that (at the cost of temporary copy creation)."""
     if not inplace:
         lst1 = lst1[:]
         lst2 = lst2[:]
     lst1.sort(); lst2.sort()
     return lst1 == lst2
 
 
 def list2dict(lst):
     """Takes a list of (key,value) pairs and turns it into a dict."""
 
     dic = {}
     for k,v in lst: dic[k] = v
     return dic
 
 
 def list2dict2(lst, default=''):
     """Takes a list and turns it into a dict.
     Much slower than list2dict, but more versatile. This version can take
     lists with sublists of arbitrary length (including sclars)."""
 
     dic = {}
     for elem in lst:
         if type(elem) in (types.ListType,types.TupleType):
             size = len(elem)
             if  size == 0:
                 pass
             elif size == 1:
                 dic[elem] = default
             else:
                 k,v = elem[0], elem[1:]
                 if len(v) == 1: v = v[0]
                 dic[k] = v
         else:
             dic[elem] = default
     return dic
 
 
 def flatten(seq):
     """Flatten a list of lists (NOT recursive, only works for 2d lists)."""
 
     return [x for subseq in seq for x in subseq]
 
 
 def get_slice(seq, start=0, stop=None, step=1):
     """Get a slice of a sequence with variable step. Specify start,stop,step."""
     if stop == None:
         stop = len(seq)
     item = lambda i: seq[i]
     return map(item,xrange(start,stop,step))
 
 
 def chop(seq, size):
     """Chop a sequence into chunks of the given size."""
     chunk = lambda i: seq[i:i+size]
     return map(chunk,xrange(0,len(seq),size))