issue_comments
4 rows where issue = 1295939038 and user = 731499 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- simple groupby_bins 10x slower than numpy · 4 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
1177245770 | https://github.com/pydata/xarray/issues/6758#issuecomment-1177245770 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GK1hK | vnoel 731499 | 2022-07-07T08:26:26Z | 2022-07-07T08:26:26Z | CONTRIBUTOR | @dcherian Just to be complete, I thought the following one-liner would work as well:
but apparently it produces slightly different results for reasons I don't understand |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
simple groupby_bins 10x slower than numpy 1295939038 | |
1177163992 | https://github.com/pydata/xarray/issues/6758#issuecomment-1177163992 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GKhjY | vnoel 731499 | 2022-07-07T06:53:52Z | 2022-07-07T06:53:52Z | CONTRIBUTOR | { "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
simple groupby_bins 10x slower than numpy 1295939038 | ||
1176777842 | https://github.com/pydata/xarray/issues/6758#issuecomment-1176777842 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GJDRy | vnoel 731499 | 2022-07-06T21:40:37Z | 2022-07-06T21:40:37Z | CONTRIBUTOR | @dcherian I just tested your numpy suggestions, and I'm getting 100x speedups compared to my naive numpy approach (~200µs vs ~20ms). Thankyouthankyouthankyou! I've been doing this for years, I can't believe I've never run into that particular solution. It's like the IDL histogram function but in numpy. I'm going to use this like crazy Thanks again |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
simple groupby_bins 10x slower than numpy 1295939038 | |
1176701867 | https://github.com/pydata/xarray/issues/6758#issuecomment-1176701867 | https://api.github.com/repos/pydata/xarray/issues/6758 | IC_kwDOAMm_X85GIwur | vnoel 731499 | 2022-07-06T20:37:12Z | 2022-07-06T20:37:12Z | CONTRIBUTOR | @dcherian this means that xarray's groupby_bins will always be slow unless flox is installed, correct? I have unfortunately little or no say on what packages are installed on the system that runs my code. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
simple groupby_bins 10x slower than numpy 1295939038 |
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