diff --git a/Python/ml_metrics/elementwise.py b/Python/ml_metrics/elementwise.py
index 1e4812a..d43826b 100644
--- a/Python/ml_metrics/elementwise.py
+++ b/Python/ml_metrics/elementwise.py
@@ -66,6 +66,29 @@ def mae(actual, predicted):
     """
     return np.mean(ae(actual, predicted))
 
+def mape(actual, predicted):
+    """
+    Computes the mean absolute percentage error.
+
+    This function computes the mean absolute percentage error between two lists
+    of numbers. 
+    BE CAREFUL: it can cause division-by-zero errors!
+
+    Parameters
+    ----------
+    actual : list of numbers, numpy array
+             The ground truth value
+    predicted : same type as actual
+                The predicted value
+
+    Returns
+    -------
+    score : double
+            The mean absolute percentage error between actual and predicted
+
+    """
+    return np.mean(np.divide(np.abs(np.array(actual) - np.array(predicted)), np.array(actual))) * 100
+
 def mse(actual, predicted):
     """
     Computes the mean squared error.
diff --git a/Python/setup.py b/Python/setup.py
index 0b742ca..71c2909 100644
--- a/Python/setup.py
+++ b/Python/setup.py
@@ -3,7 +3,10 @@
 from setuptools import setup
 import sys
 
-requirements = [x.strip() for x in open("requirements.txt")]
+requirements = [
+	"numpy", 
+	"pandas"
+	]
 
 # Automatically run 2to3 for Python 3 support
 extra = {}