concat pandas DataFrame along timeseries indexes
Solution 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
Solution 2
Try to join on outer.
When I am working with a number of stocks, I would usually have a frame titled "open high,low,close,etc" with column as a ticker. If you want one data structure, I would use Panels for this.
for Yahoo data, you can use pandas:
import pandas.io.data as data
spy = data.DataReader("SPY","yahoo","1991/1/1")
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Matthew Brown
Internationally minded, independent and driven student at QUT Business School and part-time analyst at Innovare Group looking to develop his career in the Information Technology or Management Consulting space. I'd love to chat: twitter.com/mnbbrown, [email protected] or matthewbrown.io
Updated on September 14, 2022Comments
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Matthew Brown over 1 year
I have two largish (snippets provided) pandas
DateFrame
s 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) }
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Wes McKinney over 11 yearsWhat is your pandas version? This looks like a bug that was fixed in 0.8.1
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Wes McKinney over 11 yearsCan you e-mail me pickled versions of those DataFrames (wesmckinn AT gmail)? I'm not able to reproduce the issue here. Also check that you're using either NumPy 1.6.1 or a development version after 6/5/2012. Maybe best to move this discussion to GitHub
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