FIX: Vectorized approach using NumPy searchsorted #318
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
The original code filters the calendar cal for each date in rebal_dates, which is slow for large lists due which processing taking much more time as compare to small lists and the updated code uses a vectorized search (via DatetimeIndex.searchsorted) to find all appropriate indices in one go. This will avoids explicit Python loops and repeated filtering also significantly improving performance while producing the same result for large datasets. FIX:goldmansachs#317
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Outdated suggestions cannot be applied.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.
The original code filters the calendar cal for each date in rebal_dates, which is slow for large lists due which processing taking much more time as compare to small lists and the updated code uses a vectorized search (via DatetimeIndex.searchsorted) to find all appropriate indices in one go. This will avoids explicit Python loops and repeated filtering also significantly improving performance while producing the same result for large dataset.
FIX:#317