first things first: are you sure that highcharts can handle 10*100k points 
to draw a graph ?
As for the storage, you can do anything you like: if the data doesn't 
change that much, storing into the database will be a long process only on 
the first time.
On the other end, if you need to fetch 100k records and transform them to 
json, it's going to take some time. 
Not sure on how much you'll gain from parsing i.e. a csv file instead of a 
querying a db.... 
if the returning json object is , let's say, 10 mb, it's always gonna feel 
heavy.

On Sunday, October 20, 2013 9:11:07 AM UTC+2, Thomas S wrote:
>
>
> Hi,
>
> I have created a standard application relying on Pandas and PyQt4 to 
> browse through a Pandas Dataframe.
> A dataframe is essentially a dictionary of time series data.
>
> I am new to web2py but I have experience with Pandas and matplotlib.
> I am also tempted to embed www.highcharts.com into my application.
>
> Before I dig into web2py I would like to know which route is probably most 
> promising.
>
> Should I parse the dataframe on the webserver and write it into a SQL 
> database? I guess that could be slow?
> Such a dataframe may consist of a dictionary with 100 elements each 
> several 100k points.
>
> Should I parse a time series onto request into a json format and export to 
> javaScript? 
> In this case how could I provide a way to generate a menu from the keys in 
> the dictionary.
> E.g. user clicks on a key, python does all the computations for some stats.
>
> The plan is to upload the data using csv files.
>
> So, I am a bit lost by the wide range of possibilities in web2py. I would 
> be delighted if you would like to get involved in this open source project.
> The main goal for now is to learn web2py :-)
>
> Please find the Github of the original application here
> https://github.com/tschm/PandasMonitor
>
> Sorry for being so unprecise in my questions but it just reflects that I 
> don't have a very precise plan at this stage.
>
> Kind regards
> Thomas
>

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