- 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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