ENH: __array_ufunc__ handle np.minimum.reduce #43923
Merged
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
jreback
requested changes
Nov 20, 2021
jreback
requested changes
Nov 28, 2021
| if self.ndim > 1: | ||
| if isinstance(self, ABCNDFrame): | ||
| # TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA | ||
| kwargs["numeric_only"] = False |
There was a problem hiding this comment.
do we normally modify the inputs?
There was a problem hiding this comment.
no, but that's bc the ufunc takes different kwargs than the DataFrame method
|
needs a whatsnew note |
has one |
jreback
approved these changes
Nov 29, 2021
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.mimum.reduce(frame) -> return Series instead of ndarray
np.maximum.reduce(series[dt64]) -> return Timestamp instead of np.datetime64
np.maximum.reduce(series[td64]) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(DTA) -> return Timestamp instead of np.datetime64
np.maximum.reduce(TDA) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(DTI) -> return Timestamp instead of np.datetime64
np.maximum.reduce(TDI) -> return Timedelta instead of np.timedelta64
np.maximum.reduce(integer_array) works instead of raising NotImplementedError
np.maximum.reduce(floating_array) works instead of raising NotImplementedError
np.maximum.reduce(Series[Int]) works instead of raising NotImplementedError
np.maximum.reduce(Series[Float]) works instead of raising NotImplementedError
Ditto for np.minimum.reduce