Skip to content

ENH: make the mask optional in the nullable masked ExtensionArrays #31273

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Closed
jorisvandenbossche opened this issue Jan 24, 2020 · 1 comment
Closed

ENH: make the mask optional in the nullable masked ExtensionArrays #31273

jorisvandenbossche opened this issue Jan 24, 2020 · 1 comment
Labels
Enhancement ExtensionArray Extending pandas with custom dtypes or arrays. Missing-data np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate

Comments

@jorisvandenbossche
Copy link
Member

Our nullable, mask-based ExtensionArrays (currently integer and boolean, inheriting from MaskedArray) have a _data and _mask numpy arrays stored under the hood.

A relatively straight-forward optimization is probably to allow the _mask to be None, for the case that there are no missing values.
(to be checked how involved the ops code would become to handle this as optional)
@jorisvandenbossche jorisvandenbossche added Enhancement Missing-data np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate ExtensionArray Extending pandas with custom dtypes or arrays. labels Jan 24, 2020
@jorisvandenbossche jorisvandenbossche added this to the Contributions Welcome milestone Jan 24, 2020
@jorisvandenbossche
Copy link
Member Author

Whoops forgot I already had opened an issue for this: #30435

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
Enhancement ExtensionArray Extending pandas with custom dtypes or arrays. Missing-data np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
1 participant
@jorisvandenbossche