diff --git a/quantpy/portfolio.py b/quantpy/portfolio.py
index 284b663..7124eb4 100644
--- a/quantpy/portfolio.py
+++ b/quantpy/portfolio.py
@@ -48,33 +48,23 @@ def __init__(self, symbols, start=None, end=None, bench='^GSPC'):
                 sqrt(len(tmp)) * mean(tmp.fillna(0)) / std(tmp.fillna(0))
 
     def nplot(self, symbol, color='b', nval=0):
-        if symbol == 'bench':
-            tmp = self.benchmark['Adj Close']
-        else:
-            tmp = self.asset[symbol]['Adj Close']
+        tmp = (self.benchmark if symbol == 'bench' else self.asset[symbol]) ['Adj Close'] 
         tmp = tmp / tmp[nval]
         tmp.plot(color=color, label=symbol)
         legend(loc='best', shadow=True, fancybox=True)
 
     def betas(self):
-        betas = []
-        for symbol in self.asset.keys():
-            betas.append(self.asset[symbol]['Beta'][0])
+        betas = [v['Beta'][0] for k, v in self.asset.items()]
         return Series(betas, index=self.asset.keys())
 
     def returns(self):
-        returns = []
-        for symbol in self.asset.keys():
-            returns.append(self.asset[symbol]['Return'].dropna())
+        returns = [v['Return'].dropna() for k, v in self.asset.items()]
         return Series(returns, index=self.asset.keys())
 
     def cov(self):
-        tmp = self.returns()
-        tmpl = []
-        for symbol in tmp.keys():
-            tmpl.append(tmp[symbol])
+        keys, values = self.returns().keys(), self.returns().values()
         return DataFrame(
-            cov(array(tmpl)), index=tmp.keys(), columns=tmp.keys())
+            cov(array(values)), index=keys, columns=keys) 
 
     def get_w(self, kind='sharpe'):
         V = self.cov()
@@ -83,9 +73,7 @@ def get_w(self, kind='sharpe'):
         if kind == 'characteristic':
             e = matrix(ones(len(self.asset.keys()))).T
         elif kind == 'sharpe':
-            suml = []
-            for symbol in self.asset.keys():
-                suml.append(self.returns()[symbol].sum())
+            suml = [ self.returns()[symbol].sum() for symbol in self.asset.keys()]
             e = matrix(suml).T
         else:
             print('\n  *Error: There is no weighting for kind ' + kind)
@@ -112,15 +100,14 @@ def efficient_frontier(self, xi=0.01, xf=4, npts=100, scale=10):
 
         i = 0
         rets = zeros(len(frontier))
-        risk = zeros(len(frontier))
         sharpe = zeros(len(frontier))
         for f in frontier:
             w = self.efficient_frontier_w(f)
             tmp = self.ret_for_w(w)
             rets[i] = tmp.sum() * scale
             sharpe[i] = mean(tmp) / std(tmp) * sqrt(len(tmp))
-            risk[i] = rets[i] / sharpe[i]
             i += 1
+        risk = rets/sharpe
         return Series(rets, index=risk), sharpe.max()
 
     def efficient_frontier_plot(self, xi=0.01, xf=4, npts=100, scale=0.1,
@@ -143,9 +130,7 @@ def efficient_frontier_plot(self, xi=0.01, xf=4, npts=100, scale=0.1,
 
     def min_var_w_ret(self, ret):
         V = self.cov()
-        suml = []
-        for symbol in self.asset.keys():
-            suml.append(self.returns()[symbol].sum())
+        suml = [self.returns()[symbol].sum() for symbol in self.asset.keys()] 
         e = matrix(suml).T
         iV = matrix(inv(V))
         num = iV * e * ret
@@ -154,9 +139,5 @@ def min_var_w_ret(self, ret):
 
     def ret_for_w(self, w):
         tmp = self.returns()
-        i = 0
-        tmpl = []
-        for symbol in tmp.keys():
-            tmpl.append(tmp[symbol] * w[i])
-            i += 1
+        tmpl = [v * wi for v, wi in zip(tmp.values(), w) ] 
         return Series(tmpl, index=tmp.keys()).sum()