Skip to content

Avoid using np.r_, np.c_. #16142

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.

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

Merged
merged 1 commit into from
Jan 7, 2020
Merged

Avoid using np.r_, np.c_. #16142

merged 1 commit into from
Jan 7, 2020

Conversation

anntzer
Copy link
Contributor

@anntzer anntzer commented Jan 7, 2020

They are really slow

In [3]: %timeit np.c_[np.arange(10), np.arange(10)]
11.7 µs ± 60.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [4]: %timeit np.column_stack([np.arange(10), np.arange(10)])
3.91 µs ± 154 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

and the alternative are (slightly) more readable (IMO).

PR Summary

PR Checklist

  • Has Pytest style unit tests
  • Code is Flake 8 compliant
  • New features are documented, with examples if plot related
  • Documentation is sphinx and numpydoc compliant
  • Added an entry to doc/users/next_whats_new/ if major new feature (follow instructions in README.rst there)
  • Documented in doc/api/api_changes.rst if API changed in a backward-incompatible way

They are really slow
```
In [3]: %timeit np.c_[np.arange(10), np.arange(10)]
11.7 µs ± 60.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [4]: %timeit np.column_stack([np.arange(10), np.arange(10)])
3.91 µs ± 154 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
and the alternative are (slightly) more readable (IMO).
@timhoffm timhoffm merged commit a7f136e into matplotlib:master Jan 7, 2020
@anntzer anntzer deleted the nprc branch January 7, 2020 23:47
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

Successfully merging this pull request may close these issues.

4 participants