issue_comments

Table actions

3 rows where author_association = "MEMBER" and issue = 275461273 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 3

issue 1

  • Rank function · 3 ✖

author_association 1

  • MEMBER · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
346160627 https://github.com/pydata/xarray/issues/1731#issuecomment-346160627 https://api.github.com/repos/pydata/xarray/issues/1731 MDEyOklzc3VlQ29tbWVudDM0NjE2MDYyNw== shoyer 1217238 2017-11-21T21:05:18Z 2017-11-21T21:05:18Z MEMBER

nanrankdata only support numeric types, how about wrapping rankdata with a keyword option ?

We already do dispatching to appropriate functions based on the dtype for aggregations: https://github.com/pydata/xarray/blob/9d09c1659741dafb1fadeed49c81f9e90a548b07/xarray/core/duck_array_ops.py#L174 (Yes, this is a bit of a mess)

Since NaN has a consistent sorting position in NumPy/bottleneck (it sorts to the end), I would suggest including a skipna keyword argument, like one we use for aggregation functions. Alternatively, we could use na_option : {‘keep’, ‘top’, ‘bottom’} like pandas.

There's also a push push method similar to pandas ffill which would be nice.

@jhamman is already working on ffill/bfill in https://github.com/pydata/xarray/pull/1640

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Rank function 275461273
346075273 https://github.com/pydata/xarray/issues/1731#issuecomment-346075273 https://api.github.com/repos/pydata/xarray/issues/1731 MDEyOklzc3VlQ29tbWVudDM0NjA3NTI3Mw== max-sixty 5635139 2017-11-21T16:08:22Z 2017-11-21T16:08:22Z MEMBER

Great idea.

We use bottleneck.nanrankdata manually; I don't think there's a vectorized numpy fallback

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Rank function 275461273
345918316 https://github.com/pydata/xarray/issues/1731#issuecomment-345918316 https://api.github.com/repos/pydata/xarray/issues/1731 MDEyOklzc3VlQ29tbWVudDM0NTkxODMxNg== jhamman 2443309 2017-11-21T05:06:49Z 2017-11-21T05:06:49Z MEMBER

Is there any reason not to expose a wrapper to bottleneck.nanrankdata

@0x0L - I don't think so and I think we'd be open to adding this function. Even better if there is a fallback numpy equivalent but I don't think that would be required.

I looked at the (my) whatsnew note from 0.9.2 and I it seems we decided to remove this option until there is a rank method for dataarray/dataset objects. See @shoyer's comment: https://github.com/pydata/xarray/pull/1278#discussion_r103511989

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Rank function 275461273

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]);