On Wed, Feb 22, 2017 at 8:57 AM, Alex Rogozhnikov < alex.rogozhnikov@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.