use np.cumsum and eliminate inefficient loops #60
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Hi Maintainers of this repo,
Thank you very much for your excellent work.
I am new to this repository.
I am a researcher studying the best practices of evolving data science codes. According to our findings after examining 1000 data science repositories, migration of loop-based calculations is a widespread evolution practice among developers since it improves performance and code quality. I noticed in this PR's code that it might make better use of NumPy functions and avoid unnecessary loops.
This PR is a minor contribution compared to all the hard work that you have done in this repo. However, I am hoping that it will enhance code quality and, hopefully, performance.