I have two largish (snippets provided) pandas DateFrames with unequal dates as indexes that I wish to concat into one:
NAB.AX CBA.AX
Close Volume Close Volume
Date Date
2009-06-05 36.51 4962900 2009-06-08 21.95 0
2009-06-04 36.79 5528800 2009-06-05 21.95 8917000
2009-06-03 36.80 5116500 2009-06-04 22.21 18723600
2009-06-02 36.33 5303700 2009-06-03 23.11 11643800
2009-06-01 36.16 5625500 2009-06-02 22.80 14249900
2009-05-29 35.14 13038600 --AND-- 2009-06-01 22.52 11687200
2009-05-28 33.95 7917600 2009-05-29 22.02 22350700
2009-05-27 35.13 4701100 2009-05-28 21.63 9679800
2009-05-26 35.45 4572700 2009-05-27 21.74 9338200
2009-05-25 34.80 3652500 2009-05-26 21.64 8502900
Problem is, if I run this:
keys = ['CBA.AX','NAB.AX']
mv = pandas.concat([data['CBA.AX'][650:660],data['NAB.AX'][650:660]], axis=1, keys=stocks,)
the following DateFrame is produced:
CBA.AX NAB.AX
Close Volume Close Volume
Date
2200-08-16 04:24:21.460041 NaN NaN NaN NaN
2203-05-13 04:24:21.460041 NaN NaN NaN NaN
2206-02-06 04:24:21.460041 NaN NaN NaN NaN
2208-11-02 04:24:21.460041 NaN NaN NaN NaN
2211-07-30 04:24:21.460041 NaN NaN NaN NaN
2219-10-16 04:24:21.460041 NaN NaN NaN NaN
2222-07-12 04:24:21.460041 NaN NaN NaN NaN
2225-04-07 04:24:21.460041 NaN NaN NaN NaN
2228-01-02 04:24:21.460041 NaN NaN NaN NaN
2230-09-28 04:24:21.460041 NaN NaN NaN NaN
2238-12-15 04:24:21.460041 NaN NaN NaN NaN
Does anybody have any idea why this might be the case?
On another point: is there any python libraries around that pull data from yahoo and normalise it?
Cheers.
EDIT: For reference:
data = {
'CBA.AX': <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2313 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
Data columns:
Close 2313 non-null values
Volume 2313 non-null values
dtypes: float64(1), int64(1),
'NAB.AX': <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2329 entries, 2011-12-29 00:00:00 to 2003-01-01 00:00:00
Data columns:
Close 2329 non-null values
Volume 2329 non-null values
dtypes: float64(1), int64(1)
}
It is possible to read the data with pandas and to concatenate it.
First import the data
In [449]: import pandas.io.data as web
In [450]: nab = web.get_data_yahoo('NAB.AX', start='2009-05-25',
end='2009-06-05')[['Close', 'Volume']]
In [451]: cba = web.get_data_yahoo('CBA.AX', start='2009-05-26',
end='2009-06-08')[['Close', 'Volume']]
In [453]: nab
Out[453]:
Close Volume
Date
2009-05-25 21.15 9685100
2009-05-26 21.64 8541900
2009-05-27 21.74 9042900
2009-05-28 21.63 9701000
2009-05-29 22.02 14665700
2009-06-01 22.52 6782000
2009-06-02 22.80 10473400
2009-06-03 23.11 9931400
2009-06-04 22.21 17869000
2009-06-05 21.95 8214300
In [454]: cba
Out[454]:
Close Volume
Date
2009-05-26 35.45 4529600
2009-05-27 35.13 4521500
2009-05-28 33.95 7945400
2009-05-29 35.14 12548500
2009-06-01 36.16 4509400
2009-06-02 36.33 4304900
2009-06-03 36.80 4845400
2009-06-04 36.79 4592300
2009-06-05 36.51 4417500
2009-06-08 36.51 0
Than concatenate it:
In [455]: keys = ['CBA.AX','NAB.AX']
In [456]: pd.concat([cba, nab], axis=1, keys=keys)
Out[456]:
CBA.AX NAB.AX
Close Volume Close Volume
Date
2009-05-25 NaN NaN 21.15 9685100
2009-05-26 35.45 4529600 21.64 8541900
2009-05-27 35.13 4521500 21.74 9042900
2009-05-28 33.95 7945400 21.63 9701000
2009-05-29 35.14 12548500 22.02 14665700
2009-06-01 36.16 4509400 22.52 6782000
2009-06-02 36.33 4304900 22.80 10473400
2009-06-03 36.80 4845400 23.11 9931400
2009-06-04 36.79 4592300 22.21 17869000
2009-06-05 36.51 4417500 21.95 8214300
2009-06-08 36.51 0 NaN NaN