pull_requests
5 rows where milestone = 1143506
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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 |
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37082369 | MDExOlB1bGxSZXF1ZXN0MzcwODIzNjk= | 426 | closed | 0 | Decode non-native endianness | shoyer 1217238 | Fixes #416 By the way, it turns out the simple work around for this was to install netCDF4 -- only scipy.io.netcdf returns the big-endian arrays directly. CC @bareid | 2015-06-06T01:31:14Z | 2015-06-06T03:51:14Z | 2015-06-06T03:51:13Z | 2015-06-06T03:51:13Z | 194b7def806f2d9b28ee798640ec1240cd51acbd | 0.5.1 1143506 | 0 | 66a0de7f3cd96e79fb9afa08b5650e051d7745fb | 56f688be738770b9f0d14a620d861778720e6bba | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/426 | |||
37084423 | MDExOlB1bGxSZXF1ZXN0MzcwODQ0MjM= | 427 | closed | 0 | Fix concat for identical index variables | shoyer 1217238 | Fixes #425 | 2015-06-06T04:05:10Z | 2015-06-07T06:03:23Z | 2015-06-07T06:03:16Z | 2015-06-07T06:03:16Z | acc8f38cdd1ae8a3b09e1e3fa6d31bc859a37708 | 0.5.1 1143506 | 0 | f88fb6665df3a884c8beaa788a87ca50f1652f56 | 58702a476ff6656c8924814e8a57fb93a52fde06 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/427 | |||
37376969 | MDExOlB1bGxSZXF1ZXN0MzczNzY5Njk= | 429 | closed | 0 | Add pipe method copied from pandas | shoyer 1217238 | The implementation here is directly copied from pandas: https://github.com/pydata/pandas/pull/10253 | 2015-06-10T16:19:52Z | 2015-06-11T16:45:57Z | 2015-06-11T16:45:56Z | 2015-06-11T16:45:56Z | 3d5105c7c7c9eeee1457e906ce3776e5170f2c86 | 0.5.1 1143506 | 0 | 11685a482a2ae900679715c1282170fa7c195c29 | 4c41471eb8043fc9766a850e29e0d2727c39e189 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/429 | |||
37647544 | MDExOlB1bGxSZXF1ZXN0Mzc2NDc1NDQ= | 433 | closed | 0 | Assign order | shoyer 1217238 | `xray.Dataset.assign` and `xray.Dataset.assign_coords` now assign new variables in sorted (alphabetical) order, mirroring the behavior in pandas. Previously, the order was arbitrary. | 2015-06-14T20:09:04Z | 2015-06-15T01:16:45Z | 2015-06-15T01:16:31Z | 2015-06-15T01:16:31Z | d8da2b8874a9cec2476c02603d249df14e26684d | 0.5.1 1143506 | 0 | 03e2e456d08a7680fde117e0bdccafa69d2c6f81 | c1fc55978e2a91288e9f3a002ea0eb44fcb902ba | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/433 | |||
37663007 | MDExOlB1bGxSZXF1ZXN0Mzc2NjMwMDc= | 434 | closed | 0 | One less copy when reading big-endian data with engine='scipy' | shoyer 1217238 | 2015-06-15T06:59:55Z | 2015-06-15T07:51:44Z | 2015-06-15T07:51:41Z | 2015-06-15T07:51:41Z | 4f294b58f69a7e9062d7620add6f4d03bf40faed | 0.5.1 1143506 | 0 | f8f4e98fd8e7e3952a1e6fcafbd0ab536f66ff38 | ca61217f217e524969eab650f691ee5118e179d2 | MEMBER | xarray 13221727 | https://github.com/pydata/xarray/pull/434 |
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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]);