issue_comments

Table actions

4 rows where author_association = "CONTRIBUTOR" and issue = 383945783 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: reactions, created_at (date), updated_at (date)

user 1

issue 1

  • Xarray equivalent of np.place or df.map(mapping)? · 4 ✖

author_association 1

  • CONTRIBUTOR · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
671101989 https://github.com/pydata/xarray/issues/2568#issuecomment-671101989 https://api.github.com/repos/pydata/xarray/issues/2568 MDEyOklzc3VlQ29tbWVudDY3MTEwMTk4OQ== ahuang11 15331990 2020-08-09T21:15:41Z 2020-08-09T21:15:41Z CONTRIBUTOR

If I were to make a PR, where would this method reside? Would it be under dataset.py and dataarray.py? Also, would I simply call np.select inside the method, and if so, how would I add support for dask?

My minimal example atm: ``` import xarray as xr import numpy as np import hvplot.xarray

ds = xr.tutorial.open_dataset('air_temperature').isel(time=0)

ds['air_cats'] = ( ('lat', 'lon'), np.select([ds['air'].values >= 273.15, ds['air'].values < 273.15], ['above freezing', 'below freezing']) ) ds.hvplot('lon', 'lat', hover_cols=['air_cats']) ``` 

{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray equivalent of np.place or df.map(mapping)? 383945783
599133152 https://github.com/pydata/xarray/issues/2568#issuecomment-599133152 https://api.github.com/repos/pydata/xarray/issues/2568 MDEyOklzc3VlQ29tbWVudDU5OTEzMzE1Mg== ahuang11 15331990 2020-03-14T20:48:52Z 2020-03-14T20:48:52Z CONTRIBUTOR

No, not from me at least.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray equivalent of np.place or df.map(mapping)? 383945783
441344567 https://github.com/pydata/xarray/issues/2568#issuecomment-441344567 https://api.github.com/repos/pydata/xarray/issues/2568 MDEyOklzc3VlQ29tbWVudDQ0MTM0NDU2Nw== ahuang11 15331990 2018-11-24T05:17:09Z 2018-11-24T05:25:45Z CONTRIBUTOR

Thanks for the quick replies! Is there interest in making this a built-in function? If so, I can help contribute a PR.

Also wondering about a way to wrap logic to that mapping.

Like below 0, replace with -1, between 0 and 10, replace with 5, and above 10, replace with 15 which is possible with three np.place statements I think, but have to think in backwards logic with ds.where().

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray equivalent of np.place or df.map(mapping)? 383945783
441342986 https://github.com/pydata/xarray/issues/2568#issuecomment-441342986 https://api.github.com/repos/pydata/xarray/issues/2568 MDEyOklzc3VlQ29tbWVudDQ0MTM0Mjk4Ng== ahuang11 15331990 2018-11-24T04:34:17Z 2018-11-24T04:34:17Z CONTRIBUTOR

I guess I'm thinking about more complex cases such as changing 0 -> 50, 1 -> 29, 2 -> 10

ds = xr.Dataset({'test': [0, 1, 2]}) ds.where((ds != 1) & (ds != 2), 50)

Thoughts on simplifying this?

 
{
    "total_count": 3,
    "+1": 3,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray equivalent of np.place or df.map(mapping)? 383945783

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);