Python网络数据获取（metplotlib/pandas)

sudo apt-get install python-matplotlib
sudo apt-get install python-pandas

from matplotlib.finance import quotes_historical_yahoo
from datetime import date
import pandas as pd

today = date.today()
start=(today.year,today.month-1,today.day)
quotes = quotes_historical_yahoo('CCE',start,today)
df=pd.DataFrame(quotes)
print df

monica@monica-virtual-machine:~/Python$ python quotes.py
0          1          2          3          4        5
0   736052  50.049999  50.330002  50.410000  49.970001  1226000
1   736053  50.570000  51.090000  51.130001  50.410000  1071100
2   736054  51.209999  50.740002  51.820000  50.689999  2787400
3   736055  50.419998  51.480000  51.580002  50.380001  2404500
4   736058  51.590000  52.150002  52.160000  51.509998  3175200
5   736059  52.020000  52.020000  52.490002  51.910000  3506900
6   736060  51.840000  52.680000  52.779999  51.840000  2312600
7   736061  52.520000  52.400002  52.619999  52.150002  2369500
8   736062  52.630001  52.540001  52.860001  52.410000  1392800
9   736065  52.790001  52.630001  53.200001  52.549999  2297200
10  736066  52.799999  52.820000  53.049999  52.580002  2813400
11  736067  53.090000  52.970001  53.090000  52.639999  2267500
12  736068  53.939999  53.470001  54.540001  53.360001  4120200
13  736069  53.529999  53.669998  53.779999  53.310001  1518800
14  736072  53.580002  53.590000  53.799999  53.360001  1123200
15  736073  53.820000  53.779999  53.849998  53.560001  1226600
16  736074  53.290001  52.830002  53.290001  52.360001  3085300
17  736075  53.000000  51.740002  53.000000  51.680000  3051500
18  736076  51.770000  51.830002  52.029999  51.540001  1888300
19  736079  51.720001  51.939999  52.310001  51.700001  1907400
20  736080  51.939999  51.660000  52.299999  51.580002  2601300
21  736081  51.869999  51.959999  52.279999  51.669998  2619000
22  736082  53.340000  53.900002  54.220001  53.119999  3113700
23  736083  53.650002  52.480000  53.930000  52.369999  3061000

[24 rows x 6 columns]