Metadata-Version: 2.1
Name: pybroom
Version: 0.2
Summary: Make tidy DataFrames from messy fit/model results.
Home-page: http://pybroom.readthedocs.io/
Author: Antonino Ingargiola
Author-email: tritemio@gmail.com
License: MIT
Download-URL: https://github.com/tritemio/pybroom
Keywords: dataframe tidy-data long-form model fitting tidyverse
Platform: Windows
Platform: Linux
Platform: Mac OS X
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Requires-Dist: pandas
Requires-Dist: lmfit


pybroom
=======

**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.


