Yahoo!Finance


In [4]:
import pandas.io.data as web
from datetime import datetime
start = datetime(2010, 1, 1)
end = datetime(2015, 1, 1)
f = web.DataReader('F', 'yahoo', start, end)
f.describe()


Out[4]:
Open High Low Close Volume Adj Close
count 1234.000000 1234.000000 1234.000000 1234.000000 1.234000e+03 1234.000000
mean 13.649951 13.795170 13.476280 13.636888 5.700134e+07 12.924133
std 2.490915 2.485256 2.489785 2.485954 4.137925e+07 2.550102
min 8.990000 9.030000 8.820000 8.920000 1.077030e+07 8.370000
25% 11.490000 11.662500 11.305000 11.472500 3.250235e+07 10.672500
50% 13.405000 13.580000 13.260000 13.415000 4.521715e+07 12.615000
75% 15.900000 16.017500 15.710000 15.840000 6.615830e+07 15.230000
max 18.810000 18.970000 18.610000 18.790000 4.808795e+08 17.560000

In [11]: