issue_comments: 308932098
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html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
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https://github.com/pydata/xarray/pull/1457#issuecomment-308932098 | https://api.github.com/repos/pydata/xarray/issues/1457 | 308932098 | MDEyOklzc3VlQ29tbWVudDMwODkzMjA5OA== | 5635139 | 2017-06-16T04:45:49Z | 2017-06-16T04:51:14Z | MEMBER | This is a great start! Thanks @jhamman ! Our most common performance problems are handling pandas 'oddities', like non-standard indexes. Generally when an operation that is generally vectorized becomes un-vectorized, and starts looping in python. But that's probably not a big use case for most. What are the instances others have seen performance issues? Are there ever issues with the standard transform operations, such as (addendum, I just saw the comments above): I think there's some real benefit in benchmarks to ensure we don't add code that slow down operations by an order of magnitude slower - i.e. outside the bounds of reasonable error. That's broader than optimizing around them, particularly since xarray is all python, and shouldn't be doing performance intensive work internally. |
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