Fix broken dtype set-membership check in pandas_transform_data - #12511
Open
Nityahapani wants to merge 1 commit into
Open
Fix broken dtype set-membership check in pandas_transform_data#12511Nityahapani wants to merge 1 commit into
Nityahapani wants to merge 1 commit into
Conversation
np.dtype('float32') == np.float32 is True, but they hash differently,
so 'dtype in {np.float32, np.float64}' is always False for real
pandas float32/float64 columns. Masked in practice by the 'npdtypes'
isinstance check on the same line via 'or', except on numpy <= 1.25.0
where that flag is always False -- there, every native float32/
float64 column silently takes the slower na_value-explicit path
instead of the fast one the code comment describes.
Fix: wrap the set values in np.dtype() so hashing is consistent.
Verified against real pandas: float32/float64 now correctly match,
int64 correctly still doesn't.
Member
|
I will look into this |
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
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
np.dtype('float32') == np.float32 is True, but they hash differently, so 'dtype in {np.float32, np.float64}' is always False for real pandas float32/float64 columns. Masked in practice by the 'npdtypes' isinstance check on the same line via 'or', except on numpy <= 1.25.0 where that flag is always False -- there, every native float32/ float64 column silently takes the slower na_value-explicit path instead of the fast one the code comment describes.
Fix: wrap the set values in np.dtype() so hashing is consistent.
Verified against real pandas: float32/float64 now correctly match, int64 correctly still doesn't.