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/1084#issuecomment-276719997,https://api.github.com/repos/pydata/xarray/issues/1084,276719997,MDEyOklzc3VlQ29tbWVudDI3NjcxOTk5Nw==,953992,2017-02-01T17:18:17Z,2017-02-01T17:18:17Z,MEMBER,"@spencerahill as I said above, you should not need to subclass at all, just define a new frequency,
maybe something like ``Month30`` or somesuch, which then will slot right into ``PeriodIndex``","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187591179
https://github.com/pydata/xarray/issues/1084#issuecomment-276168323,https://api.github.com/repos/pydata/xarray/issues/1084,276168323,MDEyOklzc3VlQ29tbWVudDI3NjE2ODMyMw==,953992,2017-01-30T19:43:09Z,2017-01-30T19:43:09Z,MEMBER,"@jhamman you just need a different frequency, in fact this one is pretty close: https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/offsets.py#L2257
just a matter of defining a fixed-day month frequency (numpy has this by default anyhow). PeriodIndex would then happily take this.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187591179
https://github.com/pydata/xarray/issues/1084#issuecomment-275969458,https://api.github.com/repos/pydata/xarray/issues/1084,275969458,MDEyOklzc3VlQ29tbWVudDI3NTk2OTQ1OA==,953992,2017-01-30T02:42:58Z,2017-01-30T02:42:58Z,MEMBER,just my 2c here. You are going to end up writing a huge amount of code to re-implement essentially ``PeriodIndex``. not really sure why you are going down this path. ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187591179
https://github.com/pydata/xarray/pull/702#issuecomment-171503989,https://api.github.com/repos/pydata/xarray/issues/702,171503989,MDEyOklzc3VlQ29tbWVudDE3MTUwMzk4OQ==,953992,2016-01-14T02:13:04Z,2016-01-14T02:13:04Z,MEMBER,"makes sense about dask.array.dropna
though I think you should dropna if at all possible (or have an option at least)
it IS a bit suprising to get back the full index
not sure how common that will be in practice
esp if u r stacking multiple levels
finally - think about only supporting sequential stacking as it conceptually makes more sense
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124700322
https://github.com/pydata/xarray/pull/702#issuecomment-171422543,https://api.github.com/repos/pydata/xarray/issues/702,171422543,MDEyOklzc3VlQ29tbWVudDE3MTQyMjU0Mw==,953992,2016-01-13T20:26:03Z,2016-01-13T20:26:14Z,MEMBER,"hmm, is `dask.array` dropna not implemented? I don't see why it couldn't conceptually be done (though a bit unfamiliar with the impl)
- `set_index` takes 'data' and makes it an 'index', so that is orthogonal. It would _make_ a new Coordinate. `reset_index` would do the converse.
- `stack/unstack` effectively take existing `Coordinates` and transform between them.
ok makes sense.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124700322
https://github.com/pydata/xarray/pull/702#issuecomment-171298177,https://api.github.com/repos/pydata/xarray/issues/702,171298177,MDEyOklzc3VlQ29tbWVudDE3MTI5ODE3Nw==,953992,2016-01-13T13:58:57Z,2016-01-13T13:58:57Z,MEMBER,"couple of comments:
- I think the repr, though technically accurate, is a bit misleading. lists of tuples is really only useful as a MI, so why not actually indicate that
- `stack/unstack` (as in [9]) is not idempotent, as you are reconstituting the full cartesian product of levels. This seems a bit odd though (pandas can do this because its is separately tracking what is actually in the index, via the labels), I don't think you have this though?
- these ops are really analogs of `set_index/reset_index`, rather than `stack/unstack`, so might be a bit confusing (though I think I get why you are doing it this way), it makes more sense esp for multi-dim. Maybe explain this in the pandas guide?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124700322
https://github.com/pydata/xarray/issues/701#issuecomment-168675157,https://api.github.com/repos/pydata/xarray/issues/701,168675157,MDEyOklzc3VlQ29tbWVudDE2ODY3NTE1Nw==,953992,2016-01-04T13:21:16Z,2016-01-04T13:21:16Z,MEMBER,"yeh, this is fine. maybe just note which dtypes are lossless and which are not. Yeah if you store things as `Index` objects, then this would go away.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124685682
https://github.com/pydata/xarray/issues/699#issuecomment-168565401,https://api.github.com/repos/pydata/xarray/issues/699,168565401,MDEyOklzc3VlQ29tbWVudDE2ODU2NTQwMQ==,953992,2016-01-04T02:05:33Z,2016-01-04T02:05:33Z,MEMBER,"ok, closing.
also FYI, these seem reasonable as a default.
```
In [9]: p = tm.makePanel()
In [10]: p
Out[10]:
Dimensions: 3 (items) x 30 (major_axis) x 4 (minor_axis)
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
Minor_axis axis: A to D
In [11]: p.to_xray()
Out[11]:
Dimensions: (items: 3, major_axis: 30, minor_axis: 4)
Coordinates:
* items (items) object 'ItemA' 'ItemB' 'ItemC'
* major_axis (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05 ...
* minor_axis (minor_axis) object 'A' 'B' 'C' 'D'
Data variables:
None (items, major_axis, minor_axis) float64 -0.5374 0.5918 ...
In [12]: p = tm.makePanel4D()
In [13]: p
Out[13]:
Dimensions: 3 (labels) x 3 (items) x 30 (major_axis) x 4 (minor_axis)
Labels axis: l1 to l3
Items axis: ItemA to ItemC
Major_axis axis: 2000-01-03 00:00:00 to 2000-02-11 00:00:00
Minor_axis axis: A to D
In [14]: p.to_xray()
Out[14]:
Dimensions: (items: 3, labels: 3, major_axis: 30, minor_axis: 4)
Coordinates:
* labels (labels) object 'l1' 'l2' 'l3'
* items (items) object 'ItemA' 'ItemB' 'ItemC'
* major_axis (major_axis) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05 ...
* minor_axis (minor_axis) object 'A' 'B' 'C' 'D'
Data variables:
None (labels, items, major_axis, minor_axis) float64 -0.5523 ...
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124664101
https://github.com/pydata/xarray/issues/699#issuecomment-168563184,https://api.github.com/repos/pydata/xarray/issues/699,168563184,MDEyOklzc3VlQ29tbWVudDE2ODU2MzE4NA==,953992,2016-01-04T01:33:31Z,2016-01-04T01:33:31Z,MEMBER,"ahh I c, so this is actually a 1-dim (len of 3), ok.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,124664101
https://github.com/pydata/xarray/issues/641#issuecomment-159757720,https://api.github.com/repos/pydata/xarray/issues/641,159757720,MDEyOklzc3VlQ29tbWVudDE1OTc1NzcyMA==,953992,2015-11-25T23:47:08Z,2015-11-25T23:47:08Z,MEMBER,"yep, agreed. anyhow I created a new issue for it https://github.com/pydata/pandas/issues/11704
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,113499493
https://github.com/pydata/xarray/issues/641#issuecomment-159756318,https://api.github.com/repos/pydata/xarray/issues/641,159756318,MDEyOklzc3VlQ29tbWVudDE1OTc1NjMxOA==,953992,2015-11-25T23:43:03Z,2015-11-25T23:43:03Z,MEMBER,"it's not how it's implemented
that is MUCH slower that marginal calculations
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,113499493
https://github.com/pydata/xarray/issues/641#issuecomment-159755572,https://api.github.com/repos/pydata/xarray/issues/641,159755572,MDEyOklzc3VlQ29tbWVudDE1OTc1NTU3Mg==,953992,2015-11-25T23:37:24Z,2015-11-25T23:37:24Z,MEMBER,"right, I think I will open a new issue for that. its actually a bit tricky as the iteration is done in cython itself, and its a marginal calculation anyhow (e.g. you just keep adding the new value, subtracting values that fall off the window).
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,113499493
https://github.com/pydata/xarray/issues/641#issuecomment-159754015,https://api.github.com/repos/pydata/xarray/issues/641,159754015,MDEyOklzc3VlQ29tbWVudDE1OTc1NDAxNQ==,953992,2015-11-25T23:24:09Z,2015-11-25T23:24:09Z,MEMBER,"ohh, @shoyer you are thinking about defining `__iter__` on the `Rolling`, for a custom aggregation? or other reason
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,113499493
https://github.com/pydata/xarray/issues/641#issuecomment-159753832,https://api.github.com/repos/pydata/xarray/issues/641,159753832,MDEyOklzc3VlQ29tbWVudDE1OTc1MzgzMg==,953992,2015-11-25T23:22:51Z,2015-11-25T23:22:51Z,MEMBER,"@shoyer breath holding :) https://github.com/pydata/pandas/pull/11603
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,113499493
https://github.com/pydata/xarray/issues/79#issuecomment-45769786,https://api.github.com/repos/pydata/xarray/issues/79,45769786,MDEyOklzc3VlQ29tbWVudDQ1NzY5Nzg2,953992,2014-06-11T17:03:05Z,2014-06-11T17:03:05Z,MEMBER,"FYI in the pointed to PR `joblib` does work (w/o dill actually). but `IPython.parallel` still is not working how I want it.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,29921033