diff --git a/frog-2.py b/frog-2.py
index c0b0eec..e010b2c 100644
--- a/frog-2.py
+++ b/frog-2.py
@@ -1,38 +1,42 @@
+#! python
+# I wrote this quickly in a pub
+# Don't judge me
+# You're not a bad person Matt. Say it with me... "My code is ugly, but I'm ok" --Adam
+
+from random import randrange
+import sys
+
+# if running in python 2, make range makes a list and xrange makes a generator
+# generators are faster than large lists, so use xrange
+if sys.version_info[0] == 2:
+    range = xrange
+
 def frog(final_dist=100,max=6):
+    '''
+    Simulate a large number of frogs crossing a large number of rivers. Default
+    values result in 100 million simulations.
     
-    # I wrote this quickly in a pub
-    # Don't judge me
+    final_dist: int, default=100
+        This is the width of the largest river that will be simulated
     
-    total_dist = 1
-    
-    while total_dist <= final_dist:
-        
-        import random
-        
-        cap = 10**max
+    max: int, default=6
+        This is the base ten log of the max number of trials at each distance.
+        The default of 6 therefore results in 1_000_000 trials per width of
+        river
+    '''
         
-        trials = 0
-        total_leaps = 0
-        
-        while trials <= cap:
-            
-            dist = total_dist
-            leaps = 0
-            
-            while dist > 0:
-                this_jump = int(random.random()*(dist)+1)
-                dist -= this_jump
-                leaps += 1
-                
-            total_leaps += leaps
-            
-            trials += 1
-            
-        print "{0}\t{1}".format(total_dist,(total_leaps*1.0)/trials)
-    
-        total_dist += 1
-    
-    return "DONE"
-            
+    cap = 10 ** max
+
+    def trial(total_dist):
+        '''
+        Simulate a frog crossing a river once.
+        '''
+        leaps = 0
+        while total_dist:
+            total_dist -= randrange(1, total_dist+1)
+            leaps += 1
+        return leaps
     
-    
\ No newline at end of file
+    for total_dist in range(1, final_dist + 1):
+        avg_leaps = 1.0 * sum(trial(total_dist) for _ in range(cap)) / cap
+        print("{0}\t{1}".format(total_dist, avg_leaps))