[SciPy-user] scikits.timeseries DateArray Question [timeseriesdocumentation]

Dharhas Pothina Dharhas.Pothina@twdb.state.tx...
Wed Dec 3 15:40:01 CST 2008

Thank you Tim. I've been following this package for a while. It looks really impressive. The only thing that was holding me back was installation issues and the virtualenv stuff have fixed that.

A question. Why do I need to fill missing dates? Is it required for other things like calculating daily averages etc or is there another reason?

@Pierre & Matt. Please don't my earlier emails as criticism about the documentation. I am extremely thankful that you have taken the time to develop this package. Seconding Tim, I would like to contribute examples/howto's based on the work I'm doing. If you have any guidance on how the best way to do this is that would be great.

- dharhas

>>> Tim Michelsen <timmichelsen@gmx-topmail.de> 12/3/2008 3:18 PM >>>
Hello Dharhas,
welcome as new user of timeseries user!
Learning this scikit will soon pay off.
I have seen a huge boost in the simplicity and usability of my analysis 
through the code I wrote using the timeseries.

A special praise shall be given to the developers Pierre & Matt. By 
patiently answering my "advanced python newbie" questions they really 
help me to get the maximum of the numpy.ma and scikits.timeseries tool 
box. This did really bring my numerical python coding forward.

> The documentation is a bit scarce indeed, and some functions are being  
> rewritten (eg, loadtxt). For now, here's what you can do:
I tried to add some of my questions to the cookbook. Here is one that 
may help you in the current situation:

More extensive answer - 

All other Q&A in form of mails sent in by other users and myself are at 
* creating timeseries for non convertional custom frequencies - 

Search for "time series" or timeseries 

Some answers to feature requests may also help:

Are there plans to include timeseries into the scipy online doc editor?
What for do you suggest if I would like to contribute examples here and 

> * Now, construct your time series
>  >>> series = ts.time_series([_[-1] for _ in loaded, dates=dates)
after this step you'd probably want to fill the missing dates:
series_filled = series.fill_missing_dates

=> now you can save the data to csv using reportlib from the scikit and 
to other neat things.

Hope that helps.


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