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 20:59:02 2025 |
| md5 checksum | dadd7e1fcf80f3a046694a9896cd997e |
| arch | x86_64 |
| build | py310h2f386ee_0 |
| depends | numpy >=1.21.5,<2.0a0, pandas >=1.0.5, patsy >=0.5.1, python >=3.10,<3.11.0a0, scikit-learn >=0.20.0, scipy >=1.0.0, statsmodels >=0.9.0 |
| license | BSD-3-Clause |
| license_family | BSD |
| md5 | dadd7e1fcf80f3a046694a9896cd997e |
| name | category_encoders |
| platform | linux |
| sha1 | 3ea4375ec37a7f4fe0993b9a5732366aeb2d52ca |
| sha256 | 1f9770ab336990d2c01654a99e46ec58d88e1841107e81bec4f73561b8541248 |
| size | 89105 |
| subdir | linux-64 |
| timestamp | 1659117878729 |
| version | 2.5.0 |