{"id":13569533,"url":"https://github.com/eltonlaw/impyute","last_synced_at":"2025-04-04T05:33:08.320Z","repository":{"id":44724761,"uuid":"79635757","full_name":"eltonlaw/impyute","owner":"eltonlaw","description":"Data imputations library to preprocess datasets with missing data","archived":false,"fork":false,"pushed_at":"2021-11-06T21:15:04.000Z","size":2552,"stargazers_count":352,"open_issues_count":30,"forks_count":49,"subscribers_count":11,"default_branch":"master","last_synced_at":"2024-10-02T07:21:13.010Z","etag":null,"topics":["imputation","missing-data","python","scientific-computing"],"latest_commit_sha":null,"homepage":"http://impyute.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eltonlaw.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-01-21T09:16:27.000Z","updated_at":"2024-08-25T03:33:28.000Z","dependencies_parsed_at":"2022-08-30T14:41:39.719Z","dependency_job_id":null,"html_url":"https://github.com/eltonlaw/impyute","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eltonlaw%2Fimpyute","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eltonlaw%2Fimpyute/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eltonlaw%2Fimpyute/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eltonlaw%2Fimpyute/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eltonlaw","download_url":"https://codeload.github.com/eltonlaw/impyute/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223100124,"owners_count":17087387,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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","scientific-computing"],"created_at":"2024-08-01T14:00:41.126Z","updated_at":"2024-11-05T01:32:47.331Z","avatar_url":"https://github.com/eltonlaw.png","language":"Python","funding_links":[],"categories":["Uncategorized","Others","Feature Extraction","❖ Time-Series Imputation Toolkits"],"sub_categories":["Uncategorized","General Feature Extraction","`Algorithms`"],"readme":".. image:: https://travis-ci.org/eltonlaw/impyute.svg?branch=master\n    :target: https://travis-ci.org/eltonlaw/impyute\n\n.. image:: https://img.shields.io/pypi/v/impyute.svg\n    :target: https://pypi.python.org/pypi/impyute\n\nImpyute\n========\n\nImpyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do. \n\n.. code-block:: python\n\n    \u003e\u003e\u003e n = 5\n    \u003e\u003e\u003e arr = np.random.uniform(high=6, size=(n, n))\n    \u003e\u003e\u003e for _ in range(3):\n    \u003e\u003e\u003e    arr[np.random.randint(n), np.random.randint(n)] = np.nan\n    \u003e\u003e\u003e print(arr)\n    array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, np.nan],\n           [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],\n           [0.79802036, np.nan, 0.51729349, 5.06533123, 3.70669172],\n           [1.30848217, 2.08386584, 2.29894541, np.nan, 3.38661392],\n           [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])\n    \u003e\u003e\u003e import impyute as impy\n    \u003e\u003e\u003e print(impy.mean(arr))\n    array([[0.25288643, 1.8149261 , 4.79943748, 0.54464834, 3.7122365],\n           [4.44798362, 0.93518716, 3.24430922, 2.50915032, 5.75956805],\n           [0.79802036, 1.99128649, 0.51729349, 5.06533123, 3.70669172],\n           [1.30848217, 2.08386584, 2.29894541, 3.08994336, 3.38661392],\n           [2.70989501, 3.13116687, 0.25851597, 4.24064355, 1.99607231]])\n\nFeature Support\n---------------\n\n* Imputation of Cross Sectional Data\n    * K-Nearest Neighbours\n    * Multivariate Imputation by Chained Equations\n    * Expectation Maximization\n    * Mean Imputation\n    * Mode Imputation\n    * Median Imputation\n    * Random Imputation\n* Imputation of Time Series Data\n    * Last Observation Carried Forward\n    * Moving Window\n    * Autoregressive Integrated Moving Average (WIP)\n* Diagnostic Tools\n    * Loggers\n    * Distribution of Null Values\n    * Comparison of imputations\n    * Little's MCAR Test (WIP)\n\nVersions\n--------\n\nCurrently tested on 2.7, 3.4, 3.5, 3.6 and 3.7\n\nInstallation\n------------\n\nTo install impyute, run the following:\n\n.. code-block:: bash\n\n    $ pip install impyute\n\nOr to get the most current version:\n\n.. code-block:: bash\n    \n    $ git clone https://github.com/eltonlaw/impyute\n    $ cd impyute\n    $ python setup.py install\n\nDocumentation\n-------------\n\nDocumentation is available here: http://impyute.readthedocs.io/\n\n\nHow to Contribute\n-----------------\n\nCheck out CONTRIBUTING_\n\n.. _CONTRIBUTING: https://github.com/eltonlaw/impyute/blob/master/CONTRIBUTING.md\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feltonlaw%2Fimpyute","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feltonlaw%2Fimpyute","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feltonlaw%2Fimpyute/lists"}