8000 ENH: Fast paths for richcompare by shubham11941140 · Pull Request #19720 · numpy/numpy · GitHub
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ENH: Fast paths for richcompare #19720

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ENH: Fast paths for richcompare #19720

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shubham11941140
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@shubham11941140 shubham11941140 commented Aug 20, 2021 •
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"Fixes #14415"

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There are no conflicts and all checks have passed, please merge it with the main, so I can close the Pull Request.

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Please merge the PR.

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shubham11941140 commented Aug 21, 2021 •
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@seberg please merge this PR

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Please do the merging, so I can close this issue.

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mattip commented Aug 22, 2021

@shubham11941140 as I commented before, a PR is not merged until it is reviewed. If there are no corrections or modifications needed (which is rare), and if it does not require further consideration, it may be merged. If it is not commented on for a week, then you can ping with a reminder.

Can you explain how this PR differs from #17970 ?

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mattip commented Aug 22, 2021

Since it seems the submitter is just reposting older PRs, I am going to close this. @shubham11941140 please justify this resubmission if you choose to reopen.

@mattip mattip closed this Aug 22, 2021
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I have made a few changes which I want to commit. Please open it.

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Please reopen the PR, I have a few changes to make.

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I'm just curious and asking as I opened the initial PR, is this part of some course work @shubham11941140 ? Please feel free to work on this and I can help you if needed, but any reason for taking the diff from an existing PR and pasting it almost as is without pinging the original owner?

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@ganesh-k13, this user has made 3 or 4 PRs that just copy old work without attribution. At this point, my assumption is they are acting in bad faith.

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Yeah, found his three other PRs :). I'm trying to find a pattern here. My observation so far is that these low-quality PRs are not coming from a single college. It could be some online course given we are all WFH now.

Or it could be that college students are following some trend and read somewhere that OS contribs improves their resume.

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Comparisons between numpy scalars and python scalars are very slow
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