issue_comments

Table actions

6 rows where issue = 189998469 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

author_association 2

issue 1

  • pipe, apply should call maybe_wrap_array, possibly resolve dim->axis · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
457307207 https://github.com/pydata/xarray/issues/1130#issuecomment-457307207 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDQ1NzMwNzIwNw== shoyer 1217238 2019-01-24T18:34:37Z 2019-01-24T18:34:37Z MEMBER 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469
457297015 https://github.com/pydata/xarray/issues/1130#issuecomment-457297015 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDQ1NzI5NzAxNQ== stale[bot] 26384082 2019-01-24T18:05:43Z 2019-01-24T18:05:43Z NONE

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity If this issue remains relevant, please comment here; otherwise it will be marked as closed automatically

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469
261599007 https://github.com/pydata/xarray/issues/1130#issuecomment-261599007 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDI2MTU5OTAwNw== shoyer 1217238 2016-11-18T18:03:06Z 2016-11-18T18:03:06Z MEMBER

But maybe_wrap_array is simple and does not break anything, it wraps it only when it's safe.

It's safe in the sense that the xarray.DataArray contains the information from the numpy array, but it's not safe in terms of a stable type signature.

I think that could be quite likely as one might want to apply a DataArray-compatible function. This would force users to remember which type of "function applier" to use for a given function and might be confusing.

I think this is unavoidable. It's impossible to design a single entry point that works smoothly for both use cases.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469
261496603 https://github.com/pydata/xarray/issues/1130#issuecomment-261496603 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDI2MTQ5NjYwMw== smartass101 941907 2016-11-18T10:16:17Z 2016-11-18T10:16:17Z NONE

... rather not adjust DataArray.pipe, which is intentionally very simple.

It would be just one extra call to a funciton which is very simple. As I commented in #1074, I think it makes more sense to have pipe wrap arrays, because otherwise the pipe-chain might get broken, whereas with apply I'd be ok with it behaving as the python apply function which simply applies a function and nothing more. But maybe_wrap_array is simple and does not break anything, it wraps it only when it's safe.

In the unlikely event your function takes a dim argument

I think that could be quite likely as one might want to apply a DataArray-compatible function. This would force users to remember which type of "function applier" to use for a given function and might be confusing.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469
261318464 https://github.com/pydata/xarray/issues/1130#issuecomment-261318464 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDI2MTMxODQ2NA== shoyer 1217238 2016-11-17T17:52:02Z 2016-11-17T17:52:02Z MEMBER

As for dim/axis, my inclination would be to unilaterally remap any dim keyword argument to .apply to axis using the get_axis_num. Something like:

dim = kwargs.pop('dim', None) if dim is not None: if 'axis' in kwargs: raise ValueError('cannot set both `dim` and `axis`') kwargs['axis'] = self.get_axis_num(dim)

In the unlikely event your function takes a dim argument that should not be remapped, you can pass in a lambda or functools.partial instead.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469
261317205 https://github.com/pydata/xarray/issues/1130#issuecomment-261317205 https://api.github.com/repos/pydata/xarray/issues/1130 MDEyOklzc3VlQ29tbWVudDI2MTMxNzIwNQ== shoyer 1217238 2016-11-17T17:47:12Z 2016-11-17T17:47:12Z MEMBER

On Dataset, .pipe and .apply work differently -- pipe passes the entire Dataset whereas apply passes each of the variables in turn.

I'm OK with calling maybe_wrap_array in DataArray.apply, but I'd rather not adjust DataArray.pipe, which is intentionally very simple.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pipe, apply should call maybe_wrap_array, possibly resolve dim->axis 189998469

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]);