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2 rows where repo = 13221727, "updated_at" is on date 2023-05-25 and user = 14371165 sorted by updated_at descending
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at ▲ | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type | 
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1710752209 | PR_kwDOAMm_X85QjIMH | 7844 | Improve to_dask_dataframe performance | Illviljan 14371165 | closed | 0 | 1 | 2023-05-15T20:08:24Z | 2023-05-25T20:08:54Z | 2023-05-25T20:08:54Z | MEMBER | 0 | pydata/xarray/pulls/7844 | 
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                    xarray 13221727 | pull | |||||
| 1718427036 | PR_kwDOAMm_X85Q88zP | 7857 | Avoid explicit loop when updating OrderedSet | Illviljan 14371165 | closed | 0 | 0 | 2023-05-21T09:06:58Z | 2023-05-25T20:08:35Z | 2023-05-25T20:08:34Z | MEMBER | 0 | pydata/xarray/pulls/7857 | Following recommendation from: https://github.com/pydata/xarray/pull/7824#discussion_r1196114696 ```python main:a = tuple(f"dim_{i}" for i in range(500)) %timeit OrderedSet(a) 46 µs ± 2.26 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) This PR:a = tuple(f"dim_{i}" for i in range(500)) %timeit OrderedSet(a) 28.9 µs ± 476 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) ```  | 
                
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                    xarray 13221727 | pull | 
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