Poor performance for .loc and .iloc compared to .ix
When using indices, we are encouraged to use .loc instead of .ix. Also the "SettingWithCopyWarning:" recommends us to use .loc instead.  But it seems the performance of .loc and .iloc is 20-30 times slower than .ix (I am using Pandas 0.13.1)

.ix takes 4.54897093773 sec
.iloc takes 111.531260967 sec
.loc takes 92.8014230728 sec

The costs for .loc and .iloc seems too high. Thanks!
-- test code ---
# !/usr/bin/env python

import pandas as pd
import numpy as np
import time

t=pd.DataFrame(data=np.zeros([100000,6]), columns=['A','B','C','D','E','F'])
start=time.time()
for i in t.index:
    for j in ['A','B','C','D','E']:
        t.ix[i,j]
t1=time.time()
print t1-start
for i in xrange(100000):
    for j in xrange(6):
        t.iloc[i,j]
t2=time.time()
print t2-t1
for i in t.index:
    for j in ['A','B','C','D','E']:
        t.loc[i,j]
t3=time.time()
print t3-t2
