diff --git a/Python/ml_metrics/average_precision.py b/Python/ml_metrics/average_precision.py
index e18297d..ee77e03 100644
--- a/Python/ml_metrics/average_precision.py
+++ b/Python/ml_metrics/average_precision.py
@@ -1,10 +1,11 @@
 import numpy as np
 
+
 def apk(actual, predicted, k=10):
     """
     Computes the average precision at k.
 
-    This function computes the average prescision at k between two lists of
+    This function computes the average precision at k between two lists of
     items.
 
     Parameters
@@ -22,33 +23,34 @@ def apk(actual, predicted, k=10):
             The average precision at k over the input lists
 
     """
-    if len(predicted)>k:
+    if len(predicted) > k:
         predicted = predicted[:k]
 
     score = 0.0
     num_hits = 0.0
 
-    for i,p in enumerate(predicted):
+    for i, p in enumerate(predicted):
         if p in actual and p not in predicted[:i]:
             num_hits += 1.0
-            score += num_hits / (i+1.0)
+            score += num_hits / (i + 1.0)
 
     if not actual:
         return 0.0
 
     return score / min(len(actual), k)
 
+
 def mapk(actual, predicted, k=10):
     """
     Computes the mean average precision at k.
 
-    This function computes the mean average prescision at k between two lists
+    This function computes the mean average precision at k between two lists
     of lists of items.
 
     Parameters
     ----------
     actual : list
-             A list of lists of elements that are to be predicted 
+             A list of lists of elements that are to be predicted
              (order doesn't matter in the lists)
     predicted : list
                 A list of lists of predicted elements
@@ -62,4 +64,4 @@ def mapk(actual, predicted, k=10):
             The mean average precision at k over the input lists
 
     """
-    return np.mean([apk(a,p,k) for a,p in zip(actual, predicted)])
+    return np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])