Skip to main content

Make tidy DataFrames from messy fit/model results.

Project description

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.

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-0.2.tar.gz (9.5 kB view details)

Uploaded Source

Built Distribution

pybroom-0.2-py3-none-any.whl (9.2 kB view details)

Uploaded Python 3

File details

Details for the file pybroom-0.2.tar.gz.

File metadata

  • Download URL: pybroom-0.2.tar.gz
  • Upload date:
  • Size: 9.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pybroom-0.2.tar.gz
Algorithm Hash digest
SHA256 c500af430a0c39d5fdf576e60f0a0a1f61028f144a3ba43dba26a15bdac4c395
MD5 0fb52de2eee16ae7c74cf0eaf13c0dab
BLAKE2b-256 59e269bf314fb580f81884eeb8b3b7ee78bfb025630237f4fbdd6f7ad8cf278b

See more details on using hashes here.

File details

Details for the file pybroom-0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for pybroom-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 406c33f585c9d80f307d4bc5d2f1679f6be829f7302b5ecf38141fb9d8686599
MD5 1a30fe6e3a31c568cc8cc6a30677c474
BLAKE2b-256 cfb53eafab4e85cddd1a1cc99fce24e794ffefe360acef75b68dda3a20af8827

See more details on using hashes here.

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