- 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