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  • shoyer · 4 ✖
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537747198 https://github.com/pydata/xarray/issues/3222#issuecomment-537747198 https://api.github.com/repos/pydata/xarray/issues/3222 MDEyOklzc3VlQ29tbWVudDUzNzc0NzE5OA== shoyer 1217238 2019-10-03T01:25:35Z 2019-10-03T01:25:35Z MEMBER

I think targeting 12 months of support for SciPy/pandas would be a reasonable goal.

On Wed, Oct 2, 2019 at 2:48 PM Maximilian Roos notifications@github.com wrote:

Overall this is awesome, thanks @crusaderky https://github.com/crusaderky

One minor question: do we need to support 24 months of pandas & scipy releases? These change their API faster than numpy, and I can't imagine there's a wide audience for people that a) need the very latest xarray and b) are using pandas released in 2017. I would vote to dramatically cut that, to something like the last two minor releases or 6 months. But could imagine two-three minor releases or 12 months being more palatable.

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   Minimum versions for optional libraries 481250429
537137922 https://github.com/pydata/xarray/issues/3222#issuecomment-537137922 https://api.github.com/repos/pydata/xarray/issues/3222 MDEyOklzc3VlQ29tbWVudDUzNzEzNzkyMg== shoyer 1217238 2019-10-01T17:18:08Z 2019-10-01T17:18:08Z MEMBER

Thanks for putting this together -- this looks good to me!

We can revise if any of they cause us trouble. In particular, pandas has the most stability issues, so it might be a little annoying to try to maintain two years of support.

 
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   Minimum versions for optional libraries 481250429
535152107 https://github.com/pydata/xarray/issues/3222#issuecomment-535152107 https://api.github.com/repos/pydata/xarray/issues/3222 MDEyOklzc3VlQ29tbWVudDUzNTE1MjEwNw== shoyer 1217238 2019-09-25T18:28:51Z 2019-09-25T18:28:51Z MEMBER

it would be nice to try to follow the guidelines in NEP-29: https://numpy.org/neps/nep-0029-deprecation_policy.html

 
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   Minimum versions for optional libraries 481250429
521771614 https://github.com/pydata/xarray/issues/3222#issuecomment-521771614 https://api.github.com/repos/pydata/xarray/issues/3222 MDEyOklzc3VlQ29tbWVudDUyMTc3MTYxNA== shoyer 1217238 2019-08-15T19:45:08Z 2019-08-15T19:45:08Z MEMBER

Today I tried downgrading dask in the py37 environment to dask=1.1.2, which is 6 months old...

Yikes, we should fix this.

It has not been hard to ensure that older version of NumPy and pandas work. The key thing is CI tests that verify things as PR goes in. If work-arounds for old versions are hard, then we bump minimum required dependencies.

Probably the best place to start would be adding a CI build with pinned versions of the oldest required versions that works today, and then looking if there are simple fixes that will let us downgrade project like dask. It's not worth a huge effort, but it's not very user friendly for everything to broken with old versions of optional dependencies either.

 
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   Minimum versions for optional libraries 481250429

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