ANN: pybroom 0.2 released

[Antonino Ingargiola]

Hi,

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.

Homepage: http://pybroom.readthedocs.io/

Release Notes

============

- 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.

Installation

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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).