将财务数据（用于多个股票）读入Pandas面板-演示
示例
from datetime import datetime import pandas_datareader.data as wb stocklist = ['AAPL','GOOG','FB','AMZN','COP'] start = datetime(2016,6,8) end = datetime(2016,6,11) p = wb.DataReader(stocklist, 'yahoo',start,end)
p-是一个熊猫面板，通过它我们可以做有趣的事情：
让我们看看我们的面板中有什么
In [388]: p.axes Out[388]: [Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object'), DatetimeIndex(['2016-06-08', '2016-06-09', '2016-06-10'], dtype='datetime64[ns]', name='Date', freq='D'), Index(['AAPL', 'AMZN', 'COP', 'FB', 'GOOG'], dtype='object')] In [389]: p.keys() Out[389]: Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object')
选择和切片数据
In [390]: p['Adj Close']
Out[390]:
AAPL AMZN COP FB GOOG
Date
2016-06-08 98.940002 726.640015 47.490002 118.389999 728.280029
2016-06-09 99.650002 727.650024 46.570000 118.559998 728.580017
2016-06-10 98.830002 717.909973 44.509998 116.620003 719.409973
In [391]: p['Volume']
Out[391]:
AAPL AMZN COP FB GOOG
Date
2016-06-08 20812700.0 2200100.0 9596700.0 14368700.0 1582100.0
2016-06-09 26419600.0 2163100.0 5389300.0 13823400.0 985900.0
2016-06-10 31462100.0 3409500.0 8941200.0 18412700.0 1206000.0
In [394]: p[:,:,'AAPL']
Out[394]:
Open High Low Close Volume Adj Close
Date
2016-06-08 99.019997 99.559998 98.680000 98.940002 20812700.0 98.940002
2016-06-09 98.500000 99.989998 98.459999 99.650002 26419600.0 99.650002
2016-06-10 98.529999 99.349998 98.480003 98.830002 31462100.0 98.830002
In [395]: p[:,'2016-06-10']
Out[395]:
Open High Low Close Volume Adj Close
AAPL 98.529999 99.349998 98.480003 98.830002 31462100.0 98.830002
AMZN 722.349976 724.979980 714.210022 717.909973 3409500.0 717.909973
COP 45.900002 46.119999 44.259998 44.509998 8941200.0 44.509998
FB 117.540001 118.110001 116.260002 116.620003 18412700.0 116.620003
GOOG 719.469971 725.890015 716.429993 719.409973 1206000.0 719.409973