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Wed, 22 Feb 2017 10:25:24 -0800

Hi Stephan, 
thanks for the note. The progress over last two years wasn't impressive IMO, 
but I hope you'll manage.

As you suggest, I'll have a look at xarray too, as I see xarray.Dataset. 
I was sure that it doesn't work with non-homogeneous data at all, clearly I 
need to refresh my opinion.

> 22 ֧ӧ. 2017 .,  20:55, Stephan Hoyer <> ߧѧڧѧ():
> On Wed, Feb 22, 2017 at 8:57 AM, Alex Rogozhnikov < 
> <>> wrote:
> Pandas may be nice, if you need a report, and you need get it done tomorrow. 
> Then you'll throw away the code. When we initially used pandas as main data 
> storage in yandex/rep, it looked like an good idea, but a year later it was 
> obvious this was a wrong decision. In case when you build data pipeline / 
> research that should be working several years later (using some other 
> installation by someone else), usage of pandas shall be minimal. 
> The pandas development team (myself included) is well aware of these issues. 
> There are long term plans/hopes to fix this, but there's a lot of work to be 
> done and some hard choices to make:
> <>
> <> 
>  That's why I am looking for a reliable pandas substitute, which should be: 
> - completely consistent with numpy and should fail when this wasn't 
> implemented / impossible
> - fewer new abstractions, nobody wants to learn 
> one-more-way-to-manipulate-the-data, specifically other researchers
> - it may be less convenient for interactive data mungling
>   - in particular, less methods is ok
> - written code should be interpretable, and hardly can be misinterpreted.
> - not super slow, 1-10 gigabytes datasets are a normal situation
> This has some overlap with our motivations for writing Xarray 
> ( <>), so I encourage you to 
> take a look. It still might be more complex than you're looking for, but we 
> did try to clean up the really ambiguous APIs from pandas like indexing.
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