html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/789#issuecomment-194988376,https://api.github.com/repos/pydata/xarray/issues/789,194988376,MDEyOklzc3VlQ29tbWVudDE5NDk4ODM3Ng==,1217238,2016-03-10T18:22:38Z,2016-03-10T19:46:33Z,MEMBER,"Well, the good news is that non-standard calendars like 365 are actually a bit _easier_ than the Gregorian calendar, at least if you were starting from scratch. As much as I love pushing fixes upstream, the most sane approach is to probably write a `CustomDatetimeIndex` class from scratch and start checking off boxes on [datetime functionality](http://xarray.pydata.org/en/stable/time-series.html):
- [ ] support for datetime indexing functionality (the pandas `get_indexer`, `get_loc` and `slice_indexer` methods)
- [ ] support for pulling out datetime components (e.g., year or hour)
- [ ] support for `resample` (I'm not exactly sure what the right API is here)

This might make slightly more sense in a related but distinct project to xarray.

NumPy and pandas developers will listen sympathetically, but ultimately nobody is going to work on this unless there is funding or they need it for their own work -- that's just how open source works. Fixing the underlying technology so these problems can be solved the ""right"" way is on the roadmap, but only in a vague, we'll get to it eventually kind of way.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,139956689
https://github.com/pydata/xarray/issues/789#issuecomment-194971898,https://api.github.com/repos/pydata/xarray/issues/789,194971898,MDEyOklzc3VlQ29tbWVudDE5NDk3MTg5OA==,1217238,2016-03-10T17:41:27Z,2016-03-10T17:41:27Z,MEMBER,"There are two issues here:
1. Support for years outside 1678-2262 -- blocked by pandas standardizing on nanosecond precision.
2. Support for custom calendars -- blocked by limitations of numpy's datetime64.

Unfortunately, I don't see easy fixes to either of these, though if I had to guess, adding support for other datetime precisions (perhaps only sub-second resolution) to pandas would be easier than fixing up NumPy's datetime64 itself (which is already pretty hacky).
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