Improved clarity and thoroughness of docstring.

Added design notes in comments.
Used better variable names.
Eliminated the unsavory "pool[-k:]" which was an aspiring bug (for k==0).
Used if/else to show the two algorithms in parallel style.
Added one more test assertion.

1 files changed, 41 insertions, 20 deletions

diff --git a/Lib/random.py b/Lib/random.py
index 1e832e2..f57ddb7 100644
--- a/Lib/random.py
+++ b/Lib/random.py
@@ -377,39 +377,59 @@ class Random:
     def sample(self, population, k, random=None, int=int):
         """Chooses k unique random elements from a population sequence.
 
-        Returns a new list containing elements from the population.  The
-        list itself is in random order so that all sub-slices are also
-        random samples.  The original sequence is left undisturbed.
+        Returns a new list containing elements from the population while
+        leaving the original population unchanged.  The resulting list is
+        in selection order so that all sub-slices will also be valid random
+        samples.  This allows raffle winners (the sample) to be partitioned
+        into grand prize and second place winners (the subslices).
 
-        If the population has repeated elements, then each occurrence is
-        a possible selection in the sample.
+        Members of the population need not be hashable or unique.  If the
+        population contains repeats, then each occurrence is a possible
+        selection in the sample.
 
-        If indices are needed for a large population, use xrange as an
-        argument:  sample(xrange(10000000), 60)
+        To choose a sample in a range of integers, use xrange as an argument.
+        This is especially fast and space efficient for sampling from a
+        large population:   sample(xrange(10000000), 60)
 
         Optional arg random is a 0-argument function returning a random
         float in [0.0, 1.0); by default, the standard random.random.
         """
 
+        # Sampling without replacement entails tracking either potential
+        # selections (the pool) or previous selections.
+
+        # Pools are stored in lists which provide __getitem__ for selection
+        # and provide a way to remove selections.  But each list.remove()
+        # rebuilds the entire list, so it is better to rearrange the list,
+        # placing non-selected elements at the head of the list.  Tracking
+        # the selection pool is only space efficient with small populations.
+
+        # Previous selections are stored in dictionaries which provide
+        # __contains__ for detecting repeat selections.  Discarding repeats
+        # is efficient unless most of the population has already been chosen.
+        # So, tracking selections is useful when sample sizes are much
+        # smaller than the total population.
+
         n = len(population)
         if not 0 <= k <= n:
             raise ValueError, "sample larger than population"
         if random is None:
             random = self.random
+        result = [None] * k
         if n < 6 * k:     # if n len list takes less space than a k len dict
-            pool = list(population)
-            for i in xrange(n-1, n-k-1, -1):
-                j = int(random() * (i+1))
-                pool[i], pool[j] = pool[j], pool[i]
-            return pool[-k:]
-        inorder = [None] * k
-        selections = {}
-        for i in xrange(k):
-            j = int(random() * n)
-            while j in selections:
+            pool = list(population)             # track potential selections
+            for i in xrange(k):
+                j = int(random() * (n-i))       # non-selected at [0,n-i)
+                result[i] = pool[j]             # save selected element
+                pool[j] = pool[n-i-1]           # non-selected to head of list
+        else:
+            selected = {}                       # track previous selections
+            for i in xrange(k):
                 j = int(random() * n)
-            selections[j] = inorder[i] = population[j]
-        return inorder     # return selections in the order they were picked
+                while j in selected:            # discard and replace repeats
+                    j = int(random() * n)
+                result[i] = selected[j] = population[j]
+        return result       # return selections in the order they were picked
 
 ## -------------------- real-valued distributions  -------------------
 
@@ -756,6 +776,7 @@ def _test_sample(n):
     for k in xrange(n+1):
         s = sample(population, k)
         assert len(dict([(elem,True) for elem in s])) == len(s) == k
+        assert None not in s
 
 def _sample_generator(n, k):
     # Return a fixed element from the sample.  Validates random ordering.
@@ -787,7 +808,7 @@ def _test(N=2000):
     _test_generator(N, 'weibullvariate(1.0, 1.0)')
     _test_generator(N, '_sample_generator(50, 5)')  # expected s.d.: 14.4
     _test_generator(N, '_sample_generator(50, 45)') # expected s.d.: 14.4
-    _test_sample(1000)
+    _test_sample(500)
 
     # Test jumpahead.
     s = getstate()