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 20:58:53 2025 |
| md5 checksum | f16ef4f67a930fa2051b9831042c9c6a |
| arch | x86_64 |
| build | py312h06a4308_0 |
| depends | numpy >=1.14.0, pandas >=1.0.5, patsy >=0.5.1, python >=3.12,<3.13.0a0, scikit-learn >=1.6.0, scipy >=1.0.0, statsmodels >=0.9.0 |
| license | BSD-3-Clause |
| license_family | BSD |
| md5 | f16ef4f67a930fa2051b9831042c9c6a |
| name | category_encoders |
| platform | linux |
| sha1 | 5a9ad3c2993285ebbe69d240f5763d9ffcd9d9b1 |
| sha256 | 0f5e18140e396f575f23244ce97065ae724c349604aebd9afed2d40c9c1d63c6 |
| size | 141214 |
| subdir | linux-64 |
| timestamp | 1738011289839 |
| version | 2.8.0 |