pull_requests
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| id ▼ | node_id | number | state | locked | title | user | body | created_at | updated_at | closed_at | merged_at | merge_commit_sha | assignee | milestone | draft | head | base | author_association | auto_merge | repo | url | merged_by |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 31399492 | MDExOlB1bGxSZXF1ZXN0MzEzOTk0OTI= | 378 | closed | 0 | ENH: fillna method for Dataset, DataArray and GroupBy objects | shoyer 1217238 | This is a new method for Dataset, DataArray and GroupBy objects. For the most part, it follows standard broadcasting and alignment rules for binary operations. ## Example usage Setup: ``` In [1]: import xray In [2]: import pandas as pd In [3]: import numpy as np In [4]: array = xray.DataArray(np.arange(75.0), [('time', pd.date_range('2000-01-01', periods=75, freq='5D'))]) In [5]: array[::3] = np.nan In [6]: array Out[6]: <xray.DataArray (time: 75)> array([ nan, 1., 2., nan, 4., 5., nan, 7., 8., nan, 10., 11., nan, 13., 14., nan, 16., 17., nan, 19., 20., nan, 22., 23., nan, 25., 26., nan, 28., 29., nan, 31., 32., nan, 34., 35., nan, 37., 38., nan, 40., 41., nan, 43., 44., nan, 46., 47., nan, 49., 50., nan, 52., 53., nan, 55., 56., nan, 58., 59., nan, 61., 62., nan, 64., 65., nan, 67., 68., nan, 70., 71., nan, 73., 74.]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-06 2000-01-11 2000-01-16 ... ``` Simple example: ``` In [7]: array.fillna(0) Out[7]: <xray.DataArray (time: 75)> array([ 0., 1., 2., 0., 4., 5., 0., 7., 8., 0., 10., 11., 0., 13., 14., 0., 16., 17., 0., 19., 20., 0., 22., 23., 0., 25., 26., 0., 28., 29., 0., 31., 32., 0., 34., 35., 0., 37., 38., 0., 40., 41., 0., 43., 44., 0., 46., 47., 0., 49., 50., 0., 52., 53., 0., 55., 56., 0., 58., 59., 0., 61., 62., 0., 64., 65., 0., 67., 68., 0., 70., 71., 0., 73., 74.]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-06 2000-01-11 2000-01-16 ... ``` Fill missing values with average for that month: ``` In [8]: g = array.groupby('time.month') In [9]: g.fillna(g.mean('time')) Out[9]: <xray.DataArray (time: 75)> array([ 17.2, 1. , 2. , 17.2, 4. , 5. , 17.2, 7. , 8. , 9. , 10. , 11. , 15. , 13. , 14. , 15. , 16. , 17. , … | 2015-03-18T04:16:29Z | 2015-03-20T23:00:42Z | 2015-03-20T23:00:41Z | 2015-03-20T23:00:41Z | 2369df3199638ad665539523b5ff1de89c247bf5 | 0.4.2 1028398 | 0 | 179b0b65c86400069ec25247a7c09d623966e6f6 | aaf9067d794a655e52c7186ef9e1c3388573bde6 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/378 | |||
| 31706895 | MDExOlB1bGxSZXF1ZXN0MzE3MDY4OTU= | 380 | closed | 0 | ENH: Add Dataset.assign and .assign_coords | shoyer 1217238 | Fixes #314 Based off the new pandas method of the same name. An example: ``` In [3]: ds = xray.Dataset({'y': ('x', [1, 2, 3])}) In [4]: ds.assign(z = lambda x: x.y ** 2) Out[4]: <xray.Dataset> Dimensions: (x: 3) Coordinates: * x (x) int64 0 1 2 Data variables: y (x) int64 1 2 3 z (x) int64 1 4 9 ``` | 2015-03-23T06:04:13Z | 2015-03-23T18:42:45Z | 2015-03-23T18:42:43Z | 2015-03-23T18:42:43Z | 1766cfc428d024db2038a5ba470a043a91218652 | 0.4.2 1028398 | 0 | d7271cdcbbed3c6dba9136f2b91451e455f0fcfc | 4798741a22cf24a1f132865c18e1f2320f2bc855 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/380 |
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CREATE TABLE [pull_requests] (
[id] INTEGER PRIMARY KEY,
[node_id] TEXT,
[number] INTEGER,
[state] TEXT,
[locked] INTEGER,
[title] TEXT,
[user] INTEGER REFERENCES [users]([id]),
[body] TEXT,
[created_at] TEXT,
[updated_at] TEXT,
[closed_at] TEXT,
[merged_at] TEXT,
[merge_commit_sha] TEXT,
[assignee] INTEGER REFERENCES [users]([id]),
[milestone] INTEGER REFERENCES [milestones]([id]),
[draft] INTEGER,
[head] TEXT,
[base] TEXT,
[author_association] TEXT,
[auto_merge] TEXT,
[repo] INTEGER REFERENCES [repos]([id]),
[url] TEXT,
[merged_by] INTEGER REFERENCES [users]([id])
);
CREATE INDEX [idx_pull_requests_merged_by]
ON [pull_requests] ([merged_by]);
CREATE INDEX [idx_pull_requests_repo]
ON [pull_requests] ([repo]);
CREATE INDEX [idx_pull_requests_milestone]
ON [pull_requests] ([milestone]);
CREATE INDEX [idx_pull_requests_assignee]
ON [pull_requests] ([assignee]);
CREATE INDEX [idx_pull_requests_user]
ON [pull_requests] ([user]);