Thanks for the update Carlos, good to know.

Le mardi 4 novembre 2014 00:50:27 UTC+1, Carlos Córdoba a écrit :
>
>  Hi Charles,
>
> Thanks for your suggestion. I found a way to load rows on demand, so I'll 
> add that to our next release (i.e. 2.3.2). The thing is much more snappier 
> for DataFrames with more than 100,000 rows, but it's not that good for 
> 1,000,000 ones. I don't think we can do better though :-)
>
> Cheers,
> Carlos
>
>  El 03/11/14 a las 11:23, Charles Vellutini escribió:
>  
> Thanks, interesting.
> The thing is that pandas DataFrames have become a sort of de facto 
> standard in manipulating data, at least in my area 
> (econometrics/statistics). For those who have not used them yet, DFs are 
> awesome in terms of preparing data. Still, I will look into what you 
> suggested - always good to know there are other approaches. 
>
> Le mercredi 29 octobre 2014 10:50:26 UTC+1, AndroidHiramash a écrit : 
>>
>> Hi,
>>
>> A suggestion, even if it is not directly related : generally the "io" 
>> module is used, with streams, as high level file objects.
>> There you have two solutions :
>> - either use paraview. I heard they had a Python API. Why not blend 
>> Spyder and Paraview, then...
>> - Or use HDF5 format and Python bindings. PyTables, as told on their 
>> site, has the reputation to be "blind fast" and efficient at loading while 
>> having metadata looking like pandas' dataframes. But there it is up to you 
>> at the moment to refill your data into hdf stores, I suppose ? Personnally, 
>> the hdf5 format reminds me both hyperspy library and TDMS Labview's 
>> technical data format...
>> Le 28 oct. 2014 15:20, "Charles Vellutini" <[email protected]> a 
>> écrit :
>>
>>> Hi,
>>> The addition of DataFrames as objects that can be viewed (and edited) in 
>>> Spyder's Variable Explorer is a fantastic development. Viewing data is 
>>> extremely important in serious data analysis and related debugging. With 
>>> this addition Spyder approaches the convenience and workability of 
>>> dedicated, mature statistical packages such as Stata -- all with the 
>>> performance and malleability of python. In my view, a true game changer.
>>>
>>> Now, I have noticed that the feature does not work well (yet) on large 
>>> data sets. On my system (python 3.4, 8 Go RAM), attempting to use the 
>>> Variable Explorer with a df with more than say 100,000 rows freezes Spyder 
>>> altogether. More work is needed is optimize viewing (load rows/columns only 
>>> as they are viewed, or a similar strategy?). Also, I would like to suggest 
>>> that viewing is much more important than editing -- in case it helps to 
>>> optimize the feature? Editing data through a browser is not something you 
>>> normally do - viewing data on the other hand, you do all the time. 
>>>
>>> Again congratulations on this, I truly believe that this is important 
>>> for the python data analysis community.
>>>
>>>
>>>
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