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  • Documentation on assign a value and vectorized indexing · 5 ✖

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  • CONTRIBUTOR · 5 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
385016657 https://github.com/pydata/xarray/issues/2055#issuecomment-385016657 https://api.github.com/repos/pydata/xarray/issues/2055 MDEyOklzc3VlQ29tbWVudDM4NTAxNjY1Nw== chiaral 8453445 2018-04-27T16:04:12Z 2018-04-27T16:04:12Z CONTRIBUTOR

Great, will do.

 
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   Documentation on assign a value and vectorized indexing 314239017
385016272 https://github.com/pydata/xarray/issues/2055#issuecomment-385016272 https://api.github.com/repos/pydata/xarray/issues/2055 MDEyOklzc3VlQ29tbWVudDM4NTAxNjI3Mg== chiaral 8453445 2018-04-27T16:02:44Z 2018-04-27T16:02:44Z CONTRIBUTOR

The where example might be better added to the section on where: http://xarray.pydata.org/en/v0.10.3/indexing.html#masking-with-where

I think this is not correct. the where you linked (or at least the way it is used) is for masking.

In my example uses xarray.where() to assign values.

but again, I might be off, i have a limited understanding of this.

 
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   Documentation on assign a value and vectorized indexing 314239017
385000519 https://github.com/pydata/xarray/issues/2055#issuecomment-385000519 https://api.github.com/repos/pydata/xarray/issues/2055 MDEyOklzc3VlQ29tbWVudDM4NTAwMDUxOQ== chiaral 8453445 2018-04-27T15:12:39Z 2018-04-27T15:12:39Z CONTRIBUTOR

For example, using the tutorial data:

``` ds = xr.tutorial.load_dataset('air_temperature')

add an empty 2D dataarray

ds['empty']= xr.full_like(ds.air.mean('time'),fill_value=0)

modify one grid point, using where() or loc()

ds['empty'] = xr.where((ds.coords['lat']==20)&(ds.coords['lon']==260), 100, ds['empty']) ds['empty'].loc[dict(lon=260, lat=30)] = 100

modify an area with where() and a mask

mask = (ds.coords['lat']>20)&(ds.coords['lat']<60)&(ds.coords['lon']>220)&(ds.coords['lon']<260) ds['empty'] = xr.where(mask, 100, ds['empty'])

modify an area with loc()

lc = ds.coords['lon'] la = ds.coords['lat'] ds['empty'].loc[dict(lon=lc[(lc>220)&(lc<260)], lat=la[(la>20)&(la<60)])] = 100 ```

these are examples that I am pretty sure are not on the website, they are I think common in climate scientists workflow, and that it took me quite a while to figure out. I was using a boolean dataarray as well as in the SO example, slowing down my work of quite a bit.

Do they make sense? I can try and add them to the documentation at Assigning Values with indexing , or is there another place that is more relevant? 

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   Documentation on assign a value and vectorized indexing 314239017
384989608 https://github.com/pydata/xarray/issues/2055#issuecomment-384989608 https://api.github.com/repos/pydata/xarray/issues/2055 MDEyOklzc3VlQ29tbWVudDM4NDk4OTYwOA== chiaral 8453445 2018-04-27T14:36:45Z 2018-04-27T14:36:45Z CONTRIBUTOR

I finally had the time to try out this SO suggestion on assigning on multiple dimensions as well (imaging being in need to modify the forcing of a model for a selected area) and it works. These are quite peculiar ways (at least for people not deep into xarray...) to assign values; I am compiling a list of them which IMHO should be added somewhere in the help. I will post them here for discussion, and to make sure they are indeed the most correct way to do it!

 
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   Documentation on assign a value and vectorized indexing 314239017
381254511 https://github.com/pydata/xarray/issues/2055#issuecomment-381254511 https://api.github.com/repos/pydata/xarray/issues/2055 MDEyOklzc3VlQ29tbWVudDM4MTI1NDUxMQ== chiaral 8453445 2018-04-13T20:38:09Z 2018-04-13T20:38:09Z CONTRIBUTOR

Regarding B)

I think that the current text can lead to confusion:

Select or assign values by integer location (like numpy): x[:10] or by label (like pandas): x.loc['2014-01-01'] or x.sel(time='2014-01-01').

because selecting and assigning are discussed together. I think that should be fixed too. 

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   Documentation on assign a value and vectorized indexing 314239017

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