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- Skip testing with ipywiodgets on python39: no longer supported
  since ipython 8.19

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- 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

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- Stop skipping Python 3.11.

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- 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

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- Update to 1.1.2
  * Resolve installation issue by removing import from setup.py
- Reenable python310 build, now that dask is available

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- 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

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- Skip python36 build: With NumPy 1.20, python36-numpy is no
  longer available in Tumbleweed (NEP 29)

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- 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

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- Update to 0.304:
  * Various fixes for updated dependencies

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- Update to 0.301
  * Following pandas release v1.0.0, removing deprecated keyword args "broadcast" and "reduce"

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