DataFrames in tidy format (or long-form) follow a simple rule: one “observation” per row and one “variable” per column. This simple structure makes it easy to process the data with clear and well-understood idioms (filtering, aggregation, etc.) and allows plot libraries to automatically generate complex plots in which many variables are compared. Plotting libraries supporting tidy DataFrames include seaborn, recent versions of matplotlib, bokeh and altair.
But, while data is oftentimes represented in tidy DataFrames, fit results are usually stored in a variety of custom objects and are harder to manipulate, compare and plot.
Pybroom to the rescue!¶
Pybroom allows to convert several types of fit results to tidy DataFrames, and is particularly useful for handling collections of such fit results. Pybroom development was inspired by the R library broom. You can watch this video for details of the philosophy behind broom (and by extension pybroom).
Like the R library broom, pybroom provides 3 functions: glance, tidy and augment. The glance function returns fit statistics, one for each fit result (e.g. fit method, number of iterations, chi-square etc.). The tidy function returns data for each fitted parameter (e.g. fitted value, gradient, bounds, etc.). The augment function returns data with the same size as the fitted data points (evaluated best-fit model, residuals, etc.). Additionally, pybroom has two functions tidy_to_dict and dict_to_tidy for conversion between dictionaries and 2-columns tidy DataFrames.
Collections of fit results can be in list, dict, or any nested dict/list combination. When a collection of fit result is used as input, pybroom functions return a DataFrame with additional “categorical” column(s) containing the dict keys or the list index.
Currently, supported fit result object are:
- scipy.optimize.OptimizeResult returned by several functions in scipy.optimize;
- lmfit.model.ModelResult (returned by lmfit.Model.fit());
- lmfit.minimizer.MinimizerResult (returned by lmfit.minimizer()).
Note that the 3 functions (glance, tidy and augment) are implemented only for the fit-result objects that are relevant. For example, augment cannot process lmfit’s MinimizerResult or scipy’s OptimizeResult because there is little or no data relevant to each data point.
Support for result objects from other libraries such as sklearn can be added based on user request (PR welcome!).