Skip to main content

Make tidy DataFrames from messy fit/model results.

Project description

Pybroom is a small python 3 library for converting fitting results (curve fitting or other optimizations) to Pandas DataFrame in tidy format (Wickham 2014). The 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 (for 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. pybroom development was inspired by the R library broom. See this video for details of the philosophy behind broom and pybroom.

See the pybroom homepage for more info.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pybroom-0.1rc2.tar.gz (6.1 kB view hashes)

Uploaded Source

Built Distribution

pybroom-0.1rc2-py3-none-any.whl (7.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page