Changelog for
python310-swifter-1.4.0-24.2.noarch.rpm :
* Tue Aug 01 2023 Markéta Machová
- 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 - Stop skipping Python 3.11.
* Sat Mar 25 2023 Ben Greiner - 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 AATTdiditforlulz273 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 - 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 - 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 - Skip python36 build: With NumPy 1.20, python36-numpy is no longer available in Tumbleweed (NEP 29)
* Tue Feb 09 2021 Ben Greiner - 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 - Update to 0.304:
* Various fixes for updated dependencies
* Mon Feb 10 2020 Todd R - Update to 0.301
* Following pandas release v1.0.0, removing deprecated keyword args \"broadcast\" and \"reduce\"
* Thu Jan 30 2020 Todd R - Update to 0.300
* Added new applymap method for pandas dataframes. df.swifter.applymap(...)- Update to 0.297
* Fixed issue causing errors when using swifter on empty dataframes. Now swifter will perform a pandas apply on empty dataframes.- Drop upstream-included use_current_exe.patch
