issue_comments
1 row where issue = 265056503 and user = 23199378 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Resample / upsample behavior diverges from pandas · 1 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 340535370 | https://github.com/pydata/xarray/issues/1631#issuecomment-340535370 | https://api.github.com/repos/pydata/xarray/issues/1631 | MDEyOklzc3VlQ29tbWVudDM0MDUzNTM3MA== | mmartini-usgs 23199378 | 2017-10-30T18:11:58Z | 2017-10-30T18:11:58Z | NONE | Thanks for posting this @jhamman. It's really helping me understand what is going on with my data when I use xarray. My understanding of Pandas is that it should not by default be interpolating - however I am downsampling and this is stated for upsampling (in Python for Data Analysis). |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Resample / upsample behavior diverges from pandas 265056503 |
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