Welcome to pybroom’s documentation!¶
| Latest Version: | 0.3.dev0 |
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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.
Installation¶
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 to run the example notebooks).
Documentation