issue_comments

Table actions

3 rows where issue = 108126287 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 1

  • shoyer · 3 ✖

issue 1

  • Tabular Data Packages and xray · 3 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
143236697 https://github.com/pydata/xarray/issues/587#issuecomment-143236697 https://api.github.com/repos/pydata/xarray/issues/587 MDEyOklzc3VlQ29tbWVudDE0MzIzNjY5Nw== shoyer 1217238 2015-09-25T14:21:23Z 2015-09-25T14:21:23Z MEMBER

To answer your last question, xray natively supports IO from netCDF/HDF5 and OpenDAP. We leave CSV parsing to pandas.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Tabular Data Packages and xray 108126287
143236349 https://github.com/pydata/xarray/issues/587#issuecomment-143236349 https://api.github.com/repos/pydata/xarray/issues/587 MDEyOklzc3VlQ29tbWVudDE0MzIzNjM0OQ== shoyer 1217238 2015-09-25T14:20:01Z 2015-09-25T14:20:01Z MEMBER

To map unambiguously to the xray data model, an external dataset needs to be explicitly labeled with axis names and tick labels. Pandas dataframes, for example, only make sense because they are explicitly labeled by an index (row labels).

As far as I can tell, tabular data packages does not describe data with such labels, but rather does generic tabular data. That's great, but xray is a not a tool for generic tabular data (it requires more structure), and importing this data through our interface to pandas provides a clean way to indicate this structure

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Tabular Data Packages and xray 108126287
142956698 https://github.com/pydata/xarray/issues/587#issuecomment-142956698 https://api.github.com/repos/pydata/xarray/issues/587 MDEyOklzc3VlQ29tbWVudDE0Mjk1NjY5OA== shoyer 1217238 2015-09-24T15:06:53Z 2015-09-24T15:06:53Z MEMBER

In what contexts would it make sense for xray to directly read and write these formats? We have first class support for reading/writing pandas data frames, which generally seems like a much better fit for generic tabular data.

If tabular data packages has direct support for multi-dimensional arrays then this could make sense. Otherwise, users probably should be explicitly converting to and from tabular formats.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Tabular Data Packages and xray 108126287

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]);