[SciPy-User] Status of TimeSeries SciKit

[Matt Knox]

Gael Varoquaux <gael.varoquaux <at> normalesup.org> writes:

> 
> On Tue, Jul 26, 2011 at 05:58:27PM +0000, Matt Knox wrote:
> > In many ways, the timeseries module is a giant hack which tries to work
> > around the fact that it is missing these key foundational pieces in
> > numpy.
> 
> I don't believe this statement is true. If you are doing statistics, you
> think that what is really missing in numpy is missing data support. If
> you are doing timeseries analysis, you are missing timeseries support. If
> you are doing spatial models, you are missing unstructured spatial data
> support with builtin interpolation, if you are doing general relativity,
> you are missing contra/co-variant tensor support.

Ok, perhaps my statement was a bit harsh :) . But the point I was trying to
make is that the timeseries module could be dramatically simplified and cleaned
up internally with some of those forthcoming foundational pieces in numpy,
even if the API and functionality of the timeseries module is kept identical
to what it is right now.

> My point is: let us stop dreaming that a change to core numpy will solve
> our problems. I am not saying that it cannot be improved, but in my
> opinion, the reason numpy is so successful is that it is actually the
> intersection of many different domain-specific requirements, and not the
> union.

You are right. There is no such thing as a one size fits all data structure. It
just so happens that Wes' use cases (from my understanding) are basically the
same as mine (finance, etc). So from my own selfish point of view, the idea of
pandas swallowing up the timeseries module and incorporating its functionality
sounds kind of nice since that would give ME (and probably most of the people
that work in the finance domain) an awesome swiss army knife data structure
that solves all the problems that I care about :)

- Matt Knox