Welcome to pybroom’s documentation!

Latest Version:0.2

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

• Why Pybroom?
• Pybroom’s Release Notes
• Pybroom API Documentation

Notebook Tutorials

• PyBroom Example - Simple
• PyBroom Example - Multiple Datasets
• PyBroom Example - Multiple Datasets - Minimize
• PyBroom Example - Multiple Datasets - Scipy Robust Fit

Indices and tables

• Index
• Module Index
• Search Page