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| Name: python311-swifter | Distribution: openSUSE Tumbleweed |
| Version: 1.4.0 | Vendor: openSUSE |
| Release: 2.2 | Build date: Sun Mar 10 13:16:27 2024 |
| Group: Unspecified | Build host: reproducible |
| Size: 244642 | Source RPM: python-swifter-1.4.0-2.2.src.rpm |
| Packager: https://bugs.opensuse.org | |
| Url: https://github.com/jmcarpenter2/swifter | |
| Summary: Tool to speed up pandas calculations | |
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
MIT
* Sun Mar 10 2024 Ben Greiner <code@bnavigator.de>
- Skip testing with ipywiodgets on python39: no longer supported
since ipython 8.19
* Tue Aug 01 2023 Markéta Machová <mmachova@suse.com>
- Update to 1.4.0
* Significantly reduced core dependencies of swifter library.
* Removed deprecated loffset parameter
* Updated README to be more readable for darkmode users
* Fri Jun 02 2023 Steve Kowalik <steven.kowalik@suse.com>
- Stop skipping Python 3.11.
* Sat Mar 25 2023 Ben Greiner <code@bnavigator.de>
- Update to 1.3.4
* Enable indexing after a groupby, e.g.
df.swifter.groupby(by)[key].apply(func)
* Improve groupby apply progress bar
* Previously, the groupby apply progress bar only appeared after
the data was distributed across the cores.
* Now, the groupby apply progress bar appears before the data is
distributed for a more realistic reflection of how long it took
* Additional groupby apply code refactoring and optimizations,
including removing the mutability of the data within ray
- Version 1.3.3
* Enable users to pass in df.index as the by parameter for the
df.swifter.groupby(by).apply(func) command
- Version 1.3.2
* Enable users to df.swifter.groupby.apply, which requires a new
package (ray) that now available as an extra_requires.
* To use groupby apply, install swifter as pip install -U
swifter[groupby]
* All credit goes to user @diditforlulz273 for writing the
performant groupby apply code, that is now part of swifter!
- Version 1.2.0
* Enable users to force_parallel which immediately forces swifter
to jump to using dask apply. This enables a simple interface
for parallel processing, but disables swifter's algorithm to
determine the fastest apply solution possible.
- Version 1.1.4
* Enable users to leverage set_defaults functionality so they
don't have to keep invoking individual settings on a per
swifter invocation basis
- Version 1.1.3
* Enhance the robustness of swifter by randomizing the sample
index to avoid sparse data impacting the validity of apply
validation
* Resolve issue where functions that return a non array-like
cause swifter to fail on vectorization
* Sun Mar 27 2022 Ben Greiner <code@bnavigator.de>
- Update to 1.1.2
* Resolve installation issue by removing import from setup.py
- Reenable python310 build, now that dask is available
* Mon Feb 07 2022 Ben Greiner <code@bnavigator.de>
- Update to 1.1.1
* Resolve installation issues by removing modin dependency, and
modin apply route for axis=1 string applies
* apply_dask_on_strings returns to original functionality, which
allows control over whether to use dask or pandas by default
for string applies
* Sample applies now suppress logging in addition to stdout and
stderr
* Allow new kwargs offset and origin for pandas df.resample
- Require and BuildRequire everything that is declared in the
setuptools metadata in order to avoid possible pkg_resources
failures
- Skip python310 due to python310-dask not available yet
* Sun Feb 21 2021 Ben Greiner <code@bnavigator.de>
- Skip python36 build: With NumPy 1.20, python36-numpy is no
longer available in Tumbleweed (NEP 29)
* Tue Feb 09 2021 Ben Greiner <code@bnavigator.de>
- Update to 1.0.7
* Sample applies now suppress logging in addition to stdout and
stderr
* Allow new kwargs offset and origin for pandas df.resample
- Changes in 1.0.5
* Added warnings/errors for swifter methods which do not exist
when using modin dataframes
* Updated Dask Dataframe dependencies to require a more recent
version
* Updated examples/speed benchmark notebooks
- Changes in 1.0.3
* Fixed bug with string, axis=1 applies for pandas dataframes
that prevented swifter from leveraging modin for
parallelization when returning a series instead of a dataframe
- Changes in 1.0.2
* Remove pickle5 hard dependency
- Changes in 1.0.1
* Reduce resources consumed by swifter by only importing modin/
ray when necessary.
* Added swifter.register_modin() function, which gives access to
modin.DataFrame.swifter.apply(...), but is only required if
modin is imported after swifter. If you import modin before
swifter, this is not necessary.
- Changes in 1.0.0
* Two major enhancements are included in this release, both
involving the use of modin in swifter. Special thanks to Devin
Petersohn for the collaboration.
* Enable compatibility with modin dataframes. Compatibility not
only allows modin dataframes to work with
df.swifter.apply(...), but still attempts to vectorize the
operation which can lead to a performance boost.
Example:
import modin.pandas as pd
df = pd.DataFrame(...)
df.swifter.apply(...)
* Significantly speed up swifter axis=1 string applies by using
Modin, resolving a long-standing issue for swifter.
* Use Modin for axis=1 string applies, unless
allow_dask_on_strings(True) is set. If that flag is set, still
use Dask.
NOTE: this means that allow_dask_on_strings() is no longer
required to work with text data using swifter.
- Changes in 0.305
* Remove Numba hard dependency, but still handle TypingErrors
when numba is installed
* Only call tqdm's progress_apply on transformations (e.g.
Resampler, Rolling) when tqdm has an implementation for that
object.
- Do not require modin and skip the tests involving it.
gh#jmcarpenter2/swifter#147
* Thu May 07 2020 Tomáš Chvátal <tchvatal@suse.com>
- Update to 0.304:
* Various fixes for updated dependencies
* Mon Feb 10 2020 Todd R <toddrme2178@gmail.com>
- Update to 0.301
* Following pandas release v1.0.0, removing deprecated keyword args "broadcast" and "reduce"
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