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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["imputation","missing-data","python"],"created_at":"2026-01-14T09:59:53.761Z","updated_at":"2026-01-14T09:59:54.489Z","avatar_url":"https://github.com/macarro.png","language":"Python","readme":"# imputena: impute missing values using Python\n\n[![Build Status](https://travis-ci.com/macarro/imputena.svg?branch=master)](https://travis-ci.com/macarro/imputena)\n[![Documentation Status](https://readthedocs.org/projects/imputena/badge/?version=latest)](https://imputena.readthedocs.io/en/latest/?badge=latest)\n[![Coverage Status](https://coveralls.io/repos/github/macarro/imputena/badge.svg?branch=master)](https://coveralls.io/github/macarro/imputena?branch=master)\n[![PyPI](https://img.shields.io/pypi/v/imputena)](https://pypi.org/project/imputena)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/imputena)](https://pypi.org/project/imputena)\n\nThis Python package allows both automated and customized treatment of missing\nvalues in datasets. The treatments that are implemented are:\n\n* Listwise deletion\n* Pairwise deletion\n* Dropping variables\n* Random sample imputation\n* Random hot-deck imputation\n* LOCF\n* NOCB\n* Most frequent substitution\n* Mean and median substitution\n* Constant value imputation\n* Random value imputation\n* Interpolation\n* Interpolation with seasonal adjustment\n* Linear regression imputation\n* Stochastic regression imputation\n* Logistic regression imputation\n* K-nearest neighbors imputation\n* Sequential regression multiple imputation\n* Multiple imputation by chained equations\n\nAll these treatments can be applied to whole datasets or parts of them and\n allow for extensive customization. The package can also recommend a\n  treatment for a given dataset, inform about the treatments that are\n   applicable to it, and automatically apply the best treatment.\n\n## Installation\n\n### Most recent release\n\nTo install or update to the most recently published release, run:\n\n```ShellSession\npip install imputena\n```\n\nThis will fetch the release from PyPi and install it with all dependencies.\n\n### Current development version\n\nClone this repository or download and unzip it. At the project root directory,\nrun:\n\n```ShellSession\npip install .\n```\n\n## Documentation\n\n### View online\n\nThe documentation for the latest version is available at\n[imputena.readthedocs.io](https://imputena.readthedocs.io/en/latest).\n\n### Generate locally\n\nThe documentation is generated by sphinx using the docstrings. To do so, run\neither of the following commands at the `docs` directory:\n\n```ShellSession\nmake html\nmake latexpdf\n```\n\nThe generated documentation will be located in `docs/build`.\n\n## Tests\n\nThe tests for the implemented functions are located in the `test` directory and\nuse the unittest package.\n\nTo execute all tests, run the following command at the project root directory:\n\n```ShellSession\npython -m unittest\n```\n\nTo execute only the tests contained in a particular test class, for example\n`deletion/test_delete_listwise.py`, run the following command at the\n project root directory:\n\n```ShellSession\npython -m unittest test.deletion.test_delete_listwise\n```","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacarro%2Fimputena","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmacarro%2Fimputena","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmacarro%2Fimputena/lists"}