On Wed, Feb 22, 2017 at 8:57 AM, Alex Rogozhnikov < alex.rogozhni...@yandex.ru> 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: https://github.com/pandas-dev/pandas/issues/10000 https://github.com/pandas-dev/pandas/issues/13862 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 ( http://xarray.pydata.org), 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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