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Pybroom is a small python 3+ library for converting collections of
fit results (curve fitting or other optimizations)
to Pandas <http://pandas.pydata.org/>
__
DataFrame <http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe>
__
in tidy format (or long-form)
(Wickham 2014) <http://dx.doi.org/10.18637/jss.v059.i10>
.
Once fit results are in tidy DataFrames, it is possible to leverage
common patterns <http://tomaugspurger.github.io/modern-5-tidy.html>
for tidy data analysis. Furthermore powerful visual
explorations using multi-facet plots becomes easy thanks to libraries
like seaborn <https://pypi.python.org/pypi/seaborn/>
__ natively
supporting tidy DataFrames.
See the pybroom homepage <http://pybroom.readthedocs.io/>
__ for more info.
FAQs
Make tidy DataFrames from messy fit/model results.
We found that pybroom demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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