issue_comments
3 rows where issue = 250751931 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Unexpected behavior with DataArray.resample(how='sum') in presence of NaNs · 3 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 323105006 | https://github.com/pydata/xarray/issues/1509#issuecomment-323105006 | https://api.github.com/repos/pydata/xarray/issues/1509 | MDEyOklzc3VlQ29tbWVudDMyMzEwNTAwNg== | darothen 4992424 | 2017-08-17T15:20:22Z | 2017-08-17T15:20:22Z | NONE | @betaplane a re-factoring of the |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Unexpected behavior with DataArray.resample(how='sum') in presence of NaNs 250751931 | |
| 322926397 | https://github.com/pydata/xarray/issues/1509#issuecomment-322926397 | https://api.github.com/repos/pydata/xarray/issues/1509 | MDEyOklzc3VlQ29tbWVudDMyMjkyNjM5Nw== | betaplane 5564291 | 2017-08-16T23:23:38Z | 2017-08-16T23:23:38Z | NONE | Ok, thanks a lot. Just as a comment though, pandas does it differently, and the xarray documentation states that "resample uses essentially the same api as resample in pandas." ``` In [1]: import numpy as np In [2]: import pandas as pd In [3]: x = pd.DataFrame([1, np.nan], pd.date_range('2017-08-01', '2017-08-02')) In [4]: x.resample('D').mean() Out[4]: 0 2017-08-01 1.0 2017-08-02 NaN In [5]: x.resample('D').sum() Out[5]: 0 2017-08-01 1.0 2017-08-02 NaN ``` |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Unexpected behavior with DataArray.resample(how='sum') in presence of NaNs 250751931 | |
| 322894661 | https://github.com/pydata/xarray/issues/1509#issuecomment-322894661 | https://api.github.com/repos/pydata/xarray/issues/1509 | MDEyOklzc3VlQ29tbWVudDMyMjg5NDY2MQ== | jhamman 2443309 | 2017-08-16T20:48:16Z | 2017-08-16T20:48:16Z | MEMBER | 1) I can reproduce this behavior on the current master branch
2) this is consistent with numpy's ```python In [1]: import numpy as np In [2]: np.sum([np.nan]) Out[2]: nan In [3]: np.mean([np.nan]) Out[3]: nan In [4]: np.nansum([np.nan]) Out[4]: 0.0 In [5]: np.nanmean([np.nan]) /glade/u/home/jhamman/anaconda/envs/storylines/bin/ipython:1: RuntimeWarning: Mean of empty slice #!/glade/u/home/jhamman/anaconda/envs/storylines/bin/python Out[5]: nan ``` |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Unexpected behavior with DataArray.resample(how='sum') in presence of NaNs 250751931 |
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 3