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
| Uploaded | Mon Mar 31 20:58:58 2025 |
| md5 checksum | 8534c100cfdccdc1625d92e1f19e36c8 |
| arch | x86_64 |
| build | py312h06a4308_0 |
| depends | numpy >=1.20.0,<2.0a0, pandas >=1.5.3, patsy >=0.5.1, python >=3.12,<3.13.0a0, scikit-learn >=0.20.0, scipy >=1.0.0, statsmodels >=0.9.0 |
| license | BSD-3-Clause |
| license_family | BSD |
| md5 | 8534c100cfdccdc1625d92e1f19e36c8 |
| name | category_encoders |
| platform | linux |
| sha1 | 7b48c964d0fa47fb015ed9149c5af1a67338d78c |
| sha256 | 5905557ec839ebbe8b189512330caa640849183a69985c6c4e47cfd490968eb8 |
| size | 127353 |
| subdir | linux-64 |
| timestamp | 1701756164307 |
| version | 2.6.1 |