version 0.2 of pybroom has been released. See below for the details.
What is pybroom?
Pybroom is a small python 3+ library for converting collections of fit results (curve fitting or other optimizations) to Pandas DataFrame in tidy format (or long-form) (Wickham 2014). Once fit results are in tidy DataFrames, it is possible to leverage common patterns for tidy data analysis. Furthermore powerful visual explorations using multi-facet plots becomes easy thanks to libraries like seaborn natively supporting tidy DataFrames.
- Improved support for scipy.optimize fit result. - In addition to list of fit results, pybroom now supports: - dict of fit results, - dict of lists of fit results - any other nested combination of dict and list. - When input contains a dict, pybroom adds “key” column of type pandas.Categorical.
- When input contains a list, pybroom adds a “key” column (i.e. list index) of type int64. - Updated and expanded documentation and notebooks.
You can install pybroom from PyPI using the following command:
pip install pybroom
or from conda-forge using:
conda install -c conda-forge pybroom
Dependencies are python 3.4+, pandas and lmfit (0.9.5+, which in turn requires scipy).
However, matplotlib and seaborn are strongly recommended (and necessary