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JOSS: https://github.com/scikit-learn-contrib/category_encoders

Score: 25.12641731058571

Last synced: 1 day ago
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Repository metadata:

A library of sklearn compatible categorical variable encoders

Owner metadata:

GitHub Events

Total
Last Year

Committers metadata

Last synced: 28 days ago

Total Commits: 825
Total Committers: 71
Avg Commits per committer: 11.62
Development Distribution Score (DDS): 0.816

Commits in past year: 9
Committers in past year: 4
Avg Commits per committer in past year: 2.25
Development Distribution Score (DDS) in past year: 0.667

Name Email Commits
Jan Motl j****n@m****s 152
jcastaldo08 j****8@g****m 100
Will McGinnis w****l@p****m 96
paul p****r@w****e 63
SLLiu s****6@1****m 60
florian d****n@a****h 34
Will McGinnis w****l@p****m 32
Carlos Mougan c****n@g****m 26
Lisa l****l@g****m 24
Ben Reiniger 4****r 20
PaulWestenthanner p****l@w****v 19
florian c****t@a****h 16
joshua.dunn j****n@e****m 15
makrobios b****h@g****m 15
Will McGinnis w****6 13
JaimeArboleda j****a@g****m 10
bkhant1 b****n@g****m 9
Gleb Levitski 3****v 7
anjum a****8@g****m 7
hhy h****y@1****m 7
Nicholas Bollweg n****g@g****m 6
david26694 d****4@g****m 6
taowenwu 7****9@q****m 6
Chapman Siu c****u@g****m 5
Mavs m****7@g****m 5
Rishoban r****7@g****m 5
Cameron Davison c****n@n****m 4
John Hopfensperger 4****h 4
Gijsbers p****s@t****l 4
Dennis O'Brien d****o@z****m 3
and 41 more...

Issue and Pull Request metadata

Last synced: about 1 month ago

Total issues: 101
Total pull requests: 77
Average time to close issues: over 1 year
Average time to close pull requests: 4 months
Total issue authors: 84
Total pull request authors: 31
Average comments per issue: 3.43
Average comments per pull request: 1.74
Merged pull request: 55
Bot issues: 0
Bot pull requests: 4

Past year issues: 3
Past year pull requests: 12
Past year average time to close issues: 7 months
Past year average time to close pull requests: 2 months
Past year issue authors: 3
Past year pull request authors: 7
Past year average comments per issue: 1.67
Past year average comments per pull request: 0.92
Past year merged pull request: 4
Past year bot issues: 0
Past year bot pull requests: 2

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/scikit-learn-contrib/category_encoders

Top Issue Authors

  • PaulWestenthanner (8)
  • janmotl (4)
  • bmreiniger (3)
  • wdm0006 (2)
  • eddietaylor (2)
  • JoshuaC3 (2)
  • willsthompson (2)
  • tvdboom (2)
  • DZIMDZEM (1)
  • CoteDave (1)
  • PraveshKoirala (1)
  • TobiasSackmannDacoso (1)
  • euisuk-chung (1)
  • nexusme (1)
  • iuiu34 (1)

Top Pull Request Authors

  • PaulWestenthanner (26)
  • dependabot[bot] (4)
  • bmreiniger (4)
  • wdm0006 (4)
  • glevv (4)
  • fullflu (2)
  • marekschneider (2)
  • dennisobrien (2)
  • bkhant1 (2)
  • nercisla (2)
  • Jordanbarker (2)
  • s-banach (2)
  • tvdboom (2)
  • bollwyvl (2)
  • aadimaxi (1)

Top Issue Labels

  • enhancement (22)
  • good first issue (8)
  • bug (8)
  • help wanted (7)
  • non-reproducible (6)
  • question (3)
  • discussion (3)
  • documentation (2)
  • wontfix (2)

Top Pull Request Labels

  • dependencies (4)
  • python (4)

Package metadata

conda-forge.org: category_encoders

A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. While ordinal, one-hot, and hashing encoders have similar equivalents in the existing scikit-learn version, the transformers in this library all share a few useful properties: - First-class support for pandas dataframes as an input (and optionally as output) - Can explicitly configure which columns in the data are encoded by name or index, or infer non-numeric columns regardless of input type - Can drop any columns with very low variance based on training set optionally - Portability: train a transformer on data, pickle it, reuse it later and get the same thing out. - Full compatibility with sklearn pipelines, input an array-like dataset like any other transformer

  • Homepage: https://github.com/scikit-learn-contrib/category_encoders
  • Licenses: BSD-3-Clause
  • Latest release: 2.5.0 (published almost 4 years ago)
  • Last Synced: 2026-03-23T03:13:08.074Z (about 2 months ago)
  • Versions: 16
  • Dependent Packages: 7
  • Dependent Repositories: 12
  • Downloads: 444,805 Total
  • Rankings:
    • Dependent packages count: 8.011%
    • Forks count: 8.505%
    • Stargazers count: 8.535%
    • Average: 8.798%
    • Dependent repos count: 10.142%
pypi.org: category-encoders-dev

A collection sklearn transformers to encode categorical variables as numeric

  • Homepage: https://github.com/scikit-learn-contrib/category_encoders
  • Documentation: https://category-encoders-dev.readthedocs.io/
  • Licenses: BSD
  • Latest release: 2.2.2.post2021 (published over 4 years ago)
  • Last Synced: 2026-04-14T14:01:45.619Z (30 days ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 12 Last month
  • Rankings:
    • Stargazers count: 1.473%
    • Forks count: 2.661%
    • Dependent packages count: 7.306%
    • Average: 11.524%
    • Dependent repos count: 22.077%
    • Downloads: 24.101%
  • Maintainers (1)
anaconda.org: category_encoders

A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques. While ordinal, one-hot, and hashing encoders have similar equivalents in the existing scikit-learn version, the transformers in this library all share a few useful properties: - First-class support for pandas dataframes as an input (and optionally as output) - Can explicitly configure which columns in the data are encoded by name or index, or infer non-numeric columns regardless of input type - Can drop any columns with very low variance based on training set optionally - Portability: train a transformer on data, pickle it, reuse it later and get the same thing out. - Full compatibility with sklearn pipelines, input an array-like dataset like any other transformer

  • Homepage: https://github.com/scikit-learn-contrib/category_encoders
  • Licenses: BSD-3-Clause
  • Latest release: 2.8.1 (published 10 months ago)
  • Last Synced: 2026-03-23T03:13:19.415Z (about 2 months ago)
  • Versions: 8
  • Dependent Packages: 2
  • Dependent Repositories: 12
  • Downloads: 10,195 Total
  • Rankings:
    • Stargazers count: 17.059%
    • Forks count: 17.059%
    • Dependent packages count: 20.447%
    • Average: 22.82%
    • Dependent repos count: 36.715%
nixpkgs-unstable: python314Packages.category-encoders

Library for sklearn compatible categorical variable encoders

nixpkgs-unstable: python313Packages.category-encoders

Library for sklearn compatible categorical variable encoders

Dependencies

.github/workflows/docs.yml actions
  • actions/checkout v2 composite
  • ammaraskar/sphinx-action master composite
  • peaceiris/actions-gh-pages v3 composite
.github/workflows/pypi-publish.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • pypa/gh-action-pypi-publish master composite
.github/workflows/test-docs-build.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • ammaraskar/sphinx-action master composite
.github/workflows/test-suite.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
docs/requirements.txt pypi
  • numpy >=1.14.0
  • numpydoc *
  • pandas >=0.21.1
  • patsy >=0.5.1
  • scikit-learn >=0.20.0
  • scipy >=1.0.0
  • sphinx >=3.0
  • sphinx_rtd_theme *
  • statsmodels >=0.9.0
  • unittest2 *
requirements-dev.txt pypi
  • numpydoc * development
  • pytest * development
  • sphinx * development
  • sphinx_rtd_theme * development
requirements.txt pypi
  • numpy >=1.14.0
  • pandas >=1.0.5
  • patsy >=0.5.1
  • scikit-learn >=1.0.0
  • scipy >=1.0.0
  • statsmodels >=0.9.0
  • unittest2 *
setup.py pypi
  • numpy >=1.14.0
  • pandas >=1.0.5
  • patsy >=0.5.1
  • scikit-learn >=0.20.0
  • scipy >=1.0.0
  • statsmodels >=0.9.0