issue_comments

Table actions

6 rows where issue = 46756880 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

author_association 2

issue 1

  • can't use datetime or pandas datetime to index time dimension · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
60707841 https://github.com/pydata/xarray/issues/267#issuecomment-60707841 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNzA3ODQx shoyer 1217238 2014-10-28T04:15:04Z 2014-10-28T04:15:04Z MEMBER

Closing this, but please let me know if you notice any other apparent inconsistencies.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880
60426317 https://github.com/pydata/xarray/issues/267#issuecomment-60426317 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNDI2MzE3 shoyer 1217238 2014-10-24T18:06:23Z 2014-10-24T18:06:23Z MEMBER

We pass off all label interpretation to pandas, so this discussion is really about what pandas does. The partial string selection is indeed a pandas feature: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetimeindex-partial-string-indexing

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880
60425242 https://github.com/pydata/xarray/issues/267#issuecomment-60425242 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNDI1MjQy WeatherGod 291576 2014-10-24T17:58:37Z 2014-10-24T17:58:37Z CONTRIBUTOR

So, is the string approach I used above to grab a single day's data a bug or a feature? It is a nice short-hand, but I don't want to rely on it if it isn't intended to be a feature. Similarly, if I supply a Year-Month string, I get data for that month.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880
60417045 https://github.com/pydata/xarray/issues/267#issuecomment-60417045 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNDE3MDQ1 shoyer 1217238 2014-10-24T17:01:46Z 2014-10-24T17:01:46Z MEMBER

Try supplying the exact date and time.e.g, datetime(2013, 1, 1, 11, 15) (I think that's right), or doing a slice, e.g., c.sel(time=slice(datetime(2013, 1, 1), datetime(2013, 1, 2))).

This should definitely work but pandas (reasonably) assumes that datetime objects refer to particular instants in time, unlike strings, which are less precise.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880
60413505 https://github.com/pydata/xarray/issues/267#issuecomment-60413505 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNDEzNTA1 WeatherGod 291576 2014-10-24T16:37:26Z 2014-10-24T16:37:26Z CONTRIBUTOR

Gah, I am sorry, please disregard my last comment. I can't add/subtract...

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880
60413356 https://github.com/pydata/xarray/issues/267#issuecomment-60413356 https://api.github.com/repos/pydata/xarray/issues/267 MDEyOklzc3VlQ29tbWVudDYwNDEzMzU2 WeatherGod 291576 2014-10-24T16:36:18Z 2014-10-24T16:36:18Z CONTRIBUTOR

A bit of a further wrinkle is that date selection seems to be limited to local time only because of this limitation. Consider the following:

```

c['time'][:25] <xray.DataArray 'time' (time: 25)> array(['2013-01-01T06:15:00.000000000-0500', '2013-01-01T07:00:00.000000000-0500', '2013-01-01T08:00:00.000000000-0500', '2013-01-01T09:00:00.000000000-0500', '2013-01-01T10:00:00.000000000-0500', '2013-01-01T11:00:00.000000000-0500', '2013-01-01T12:00:00.000000000-0500', '2013-01-01T13:00:00.000000000-0500', '2013-01-01T14:00:00.000000000-0500', '2013-01-01T15:00:00.000000000-0500', '2013-01-01T16:00:00.000000000-0500', '2013-01-01T17:00:00.000000000-0500', '2013-01-01T18:00:00.000000000-0500', '2013-01-01T19:00:00.000000000-0500', '2013-01-01T20:00:00.000000000-0500', '2013-01-01T21:00:00.000000000-0500', '2013-01-01T22:00:00.000000000-0500', '2013-01-01T23:00:00.000000000-0500', '2013-01-02T00:00:00.000000000-0500', '2013-01-02T01:00:00.000000000-0500', '2013-01-02T02:00:00.000000000-0500', '2013-01-02T03:00:00.000000000-0500', '2013-01-02T04:00:00.000000000-0500', '2013-01-02T05:00:00.000000000-0500', '2013-01-02T06:00:00.000000000-0500'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2013-01-01T11:15:00 ... latitude float32 64.833 elevation float32 137.5 longitude float32 -147.6 c.sel(time='2013-01-01')['time'] <xray.DataArray 'time' (time: 13)> array(['2013-01-01T06:15:00.000000000-0500', '2013-01-01T07:00:00.000000000-0500', '2013-01-01T08:00:00.000000000-0500', '2013-01-01T09:00:00.000000000-0500', '2013-01-01T10:00:00.000000000-0500', '2013-01-01T11:00:00.000000000-0500', '2013-01-01T12:00:00.000000000-0500', '2013-01-01T13:00:00.000000000-0500', '2013-01-01T14:00:00.000000000-0500', '2013-01-01T15:00:00.000000000-0500', '2013-01-01T16:00:00.000000000-0500', '2013-01-01T17:00:00.000000000-0500', '2013-01-01T18:00:00.000000000-0500'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2013-01-01T11:15:00 ... latitude float32 64.833 elevation float32 137.5 longitude float32 -147.6 ```

I don't know how I would (easily) slice this data array such as to grab only data for a UTC day.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   can't use datetime or pandas datetime to index time dimension 46756880

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]);