issue_comments
2 rows where author_association = "MEMBER" and issue = 88868867 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Working with labeled N-dimensional data with combinatoric independent variables · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
112621215 | https://github.com/pydata/xarray/issues/435#issuecomment-112621215 | https://api.github.com/repos/pydata/xarray/issues/435 | MDEyOklzc3VlQ29tbWVudDExMjYyMTIxNQ== | shoyer 1217238 | 2015-06-17T01:45:18Z | 2015-06-17T01:45:18Z | MEMBER | To elaborate: even though both pandas and xray use numpy under the hood, I suspect you may see a performance benefit if you switch from pandas to xray, for three reasons: 1. as you noted, you will no longer need repeats for all those independent variables 2. flattening to put things in a 1D column can require a copy (if the data is not already C-contiguous) 3. pandas also often makes copies when you add new dataframe columns, because it tries to consolidate adjacent columns into the same type To answer your other question about retrieving results for specific conditions: once you put things in xray dataset, that should be as simple as |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Working with labeled N-dimensional data with combinatoric independent variables 88868867 | |
112617486 | https://github.com/pydata/xarray/issues/435#issuecomment-112617486 | https://api.github.com/repos/pydata/xarray/issues/435 | MDEyOklzc3VlQ29tbWVudDExMjYxNzQ4Ng== | shoyer 1217238 | 2015-06-17T01:10:45Z | 2015-06-17T01:10:45Z | MEMBER | I suspect that an If each of the columns in the As for iterative updates, arrays in xray objects can be efficiently modified in place just like numpy arrays. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Working with labeled N-dimensional data with combinatoric independent variables 88868867 |
Advanced export
JSON shape: default, array, newline-delimited, object
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]);
user 1