issue_comments

Table actions

4 rows where author_association = "MEMBER" and issue = 33559045 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 2

issue 1

  • Wrap bottleneck for fast moving window aggregations · 4 ✖

author_association 1

  • MEMBER · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
186492043 https://github.com/pydata/xarray/issues/130#issuecomment-186492043 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDE4NjQ5MjA0Mw== jhamman 2443309 2016-02-20T02:35:09Z 2016-02-20T02:35:09Z MEMBER

closed via #668

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Wrap bottleneck for fast moving window aggregations 33559045
132776633 https://github.com/pydata/xarray/issues/130#issuecomment-132776633 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDEzMjc3NjYzMw== shoyer 1217238 2015-08-19T20:41:28Z 2015-08-19T20:41:28Z MEMBER

There's been some related discussion about a better API for the rolling aggregation functions in pandas recently: https://github.com/pydata/pandas/pull/10702

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Wrap bottleneck for fast moving window aggregations 33559045
116411021 https://github.com/pydata/xarray/issues/130#issuecomment-116411021 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDExNjQxMTAyMQ== shoyer 1217238 2015-06-29T03:22:13Z 2015-08-19T20:01:41Z MEMBER

Some further thoughts: - I would probably implement these as methods on Dataset/DataArray (e.g., ds.rolling_mean(dim='time', window=5) instead of functions. This is different from pandas, but seems slightly more in line with how everything else in pandas/xray operates. - I don't have a strong opinion whether we use the name rolling (like pandas) or moving/move (like bottleneck). Maybe rolling is slightly more obvious? - These functions from bottleneck could be easily extended to work on dask arrays by using using ghost: http://dask.pydata.org/en/latest/ghost.html

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Wrap bottleneck for fast moving window aggregations 33559045
74933558 https://github.com/pydata/xarray/issues/130#issuecomment-74933558 https://api.github.com/repos/pydata/xarray/issues/130 MDEyOklzc3VlQ29tbWVudDc0OTMzNTU4 shoyer 1217238 2015-02-18T19:44:13Z 2015-02-18T19:44:13Z MEMBER

Wrapping bottleneck for operations like nanmean was fixed in #304.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Wrap bottleneck for fast moving window aggregations 33559045

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]);