diff --git a/python/Recursion/celebrity-problem.py b/python/Recursion/celebrity-problem.py
index 197a63b..0fea665 100644
--- a/python/Recursion/celebrity-problem.py
+++ b/python/Recursion/celebrity-problem.py
@@ -1,4 +1,4 @@
-##!/usr/bin/env python
+# !/usr/bin/env python
 # -*- coding: utf-8 -*-
 
 """
@@ -12,33 +12,42 @@
 
 from random import randrange
 
+
 def celeb(G):
     n = len(G)
-    
-    u,v = 0,1
-    for c in range(2,n+1):
-        if G[u][v]: u=c
-        else: v=c
-    
-    if u==n: c=v
-    else:   c=u
-    
+
+    u, v = 0, 1
+    for c in range(2, n + 1):
+        if G[u][v]:
+            u = c
+        else:
+            v = c
+
+    if u == n:
+        c = v
+    else:
+        c = u
+
     for v in range(n):
-        if c==v: continue
-        if G[c][v]: break
-        if not G[v][c]: break
+        if c == v:
+            continue
+        if G[c][v]:
+            break
+        if not G[v][c]:
+            break
     else:
         return c
     return None
 
+
 if __name__ == "__main__":
     n = 100
-    G  = [[randrange(2) for i in range(n)] for i in range(n)]
-    
+    G = [[randrange(2) for i in range(n)] for i in range(n)]
+
     c = randrange(n)
-    
+
     for i in range(n):
         G[i][c] = True
         G[c][i] = False
-    
-    print(celeb(G))
\ No newline at end of file
+
+    print(celeb(G))
diff --git a/python/Recursion/top-sort.py b/python/Recursion/top-sort.py
index d315de9..fd64db2 100644
--- a/python/Recursion/top-sort.py
+++ b/python/Recursion/top-sort.py
@@ -1,4 +1,4 @@
-##!/usr/bin/env python
+# !/usr/bin/env python
 # -*- coding: utf-8 -*-
 
 """
@@ -13,75 +13,71 @@
 from random import randrange
 from collections import defaultdict
 
-def naive_topsort(G,S=None):
-    if S is None: S = set(G)
-    
-    if len(S) == 1: return list(S)
-    
+
+def naive_topsort(G, S=None):
+    if S is None:
+        S = set(G)
+
+    if len(S) == 1:
+        return list(S)
+
     v = S.pop()
-    
-    seq = naive_topsort(G,S)
-    
+
+    seq = naive_topsort(G, S)
+
     min_i = 0
-    for i,u in enumerate(seq):
-        if v in G[u]: min_i = i+1
-    seq.insert(min_i,v)
+    for i, u in enumerate(seq):
+        if v in G[u]:
+            min_i = i + 1
+    seq.insert(min_i, v)
     return seq
 
 
 def topsort(G):
-    count = dict((u, 0) for u in G) # The in-degree for each node
+    count = dict((u, 0) for u in G)  # The in-degree for each node
     for u in G:
         for v in G[u]:
-            count[v] += 1 # Count every in-edge
-    Q = [u for u in G if count[u] == 0] # Valid initial nodes
-    S = [] # The result
-    while Q: # While we have start nodes...
-        u = Q.pop() # Pick one
-        S.append(u) # Use it as first of the rest
+            count[v] += 1  # Count every in-edge
+    Q = [u for u in G if count[u] == 0]  # Valid initial nodes
+    S = []  # The result
+    while Q:  # While we have start nodes...
+        u = Q.pop()  # Pick one
+        S.append(u)  # Use it as first of the rest
         for v in G[u]:
-            count[v] -= 1 # "Uncount" its out-edges
-            if count[v] == 0: # New valid start nodes?
-                Q.append(v) # Deal with them next
+            count[v] -= 1  # "Uncount" its out-edges
+            if count[v] == 0:  # New valid start nodes?
+                Q.append(v)  # Deal with them next
     return S
 
 
 class randRangesparse(object):
     """docstring for randRangesparse."""
+
     def __init__(self, n):
         super(randRangesparse, self).__init__()
         self.n = n
         self.weights = self.regenerate_weights()
 
     def regenerate_weights(self):
-        return {i:self.n-i for i in range(self.n)}
-        
+        return {i: self.n - i for i in range(self.n)}
+
     def get_random(self):
         coin = randrange(self.n)
 
         while self.weights[coin] != 0:
             if self.weights[coin] != 0:
-                self.weights[coin]-=1
+                self.weights[coin] -= 1
                 return coin
             else:
-                coin = randrange(self.n-1)
+                coin = randrange(self.n - 1)
 
         self.weights = self.regenerate_weights()
         return self.get_random()
-        
-        
-        
 
-        
 
 if __name__ == "__main__":
-    
-    rng = randRangesparse(10)
-    G = [[rng.get_random() for _ in range(10)]  for _ in range(10)]
-    
-    print(naive_topsort(G))
-    
 
+    rng = randRangesparse(10)
+    G = [[rng.get_random() for _ in range(10)] for _ in range(10)]
 
-        
-    
+    print(naive_topsort(G))