[SciPy-User] Status of TimeSeries SciKit

[Wes McKinney]

On Wed, Jul 27, 2011 at 6:31 PM, Tim Michelsen
<timmichelsen at gmx-topmail.de> wrote:
> Hello all,
> similar to Dharhas, I was a strong user of the time series scikit from
> the very beginning.
> Since most of my code for meteorological data evaluations is based on
> it, I would be happy to receive infomation on the conclusion and how I
> need to adjust my code to upkeep with new developments.

When it gets to that point I'd be happy to help (including looking at
some of your existing code and data).

>>>>>> - full missing value support (TimeSeries class is a subclass of MaskedArray)
>>>>>
>>>>> I challenge you to find a (realistic) use case where the missing value
>>>>> support in pandas in inadequate. I'm being completely serious =) But
>>>>> I've been very vocal about my dislike of MaskedArrays in the missing
>>>>> data discussions. They're hard for (normal) people to use, degrade
>>>>> performance, use extra memory, etc. They add a layer of complication
>>>>> for working with time series that strikes me as completely
>>>>> unnecessary.
>>
>> </sigh>
>> Let's wait a bit and see how missing/ignored values are getting supported, shall we ?
> How does Pandas deal with missing values?

discussed a bit in my reply here:

http://article.gmane.org/gmane.comp.python.scientific.user/29661

In short using NaN across the dtypes with special functions
isnull/notnull to detect NaN in dtype=object arrays. I'm hopeful this
can be replaced with native NumPy NA support in the relatively near
future...

> This pages:
> http://pandas.sourceforge.net/missing_data.html?highlight=missing
> Is empty
>
> The convenient support for missing data (once date converterters were
> out) in timeseries helps a lot to quickly deal with measurement logs or
> incomplete data.
>
> Best regards,
> Timmie
>
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