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 | Tue Apr 1 01:04:55 2025 |
| md5 checksum | 0e12918b81a74a7d443fe57e3e9f7e05 |
| build | pyhd3eb1b0_0 |
| depends | numpy >=1.14.0, pandas >=0.21.1, patsy >=0.5.1, python >=3.5, scikit-learn >=0.20.0, scipy >=1.0.0, statsmodels >=0.9.0 |
| license | BSD-3-Clause |
| license_family | BSD |
| md5 | 0e12918b81a74a7d443fe57e3e9f7e05 |
| name | category_encoders |
| noarch | python |
| sha1 | 6094bf50f0c98ac2f9b82fc99c98bf15c02e17bd |
| sha256 | 0e5565eb1c38f5a556fadfdefee8f1081ff633724f7461bbae976b4745ca50ec |
| size | 59755 |
| subdir | noarch |
| timestamp | 1633413662568 |
| version | 2.2.2 |