{"id":15973699,"url":"https://github.com/c-bata/outlier-utils","last_synced_at":"2025-05-07T18:09:23.020Z","repository":{"id":35544110,"uuid":"39815483","full_name":"c-bata/outlier-utils","owner":"c-bata","description":"Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.","archived":false,"fork":false,"pushed_at":"2023-09-07T04:45:01.000Z","size":42,"stargazers_count":56,"open_issues_count":3,"forks_count":18,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-05-07T18:08:57.013Z","etag":null,"topics":["outliers","python","statistics"],"latest_commit_sha":null,"homepage":"https://pypi.python.org/pypi/outlier-utils","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/c-bata.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGES.rst","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2015-07-28T05:46:54.000Z","updated_at":"2024-11-10T17:07:27.000Z","dependencies_parsed_at":"2023-02-18T14:30:25.662Z","dependency_job_id":"72b89151-d082-4a01-b082-10474e99389f","html_url":"https://github.com/c-bata/outlier-utils","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/c-bata%2Foutlier-utils","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/c-bata%2Foutlier-utils/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/c-bata%2Foutlier-utils/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/c-bata%2Foutlier-utils/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/c-bata","download_url":"https://codeload.github.com/c-bata/outlier-utils/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252931555,"owners_count":21827112,"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":["outliers","python","statistics"],"created_at":"2024-10-07T21:06:30.912Z","updated_at":"2025-05-07T18:09:22.965Z","avatar_url":"https://github.com/c-bata.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"=============\noutlier-utils\n=============\n\nUtility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs_ test.\n\nRequirements\n------------\n\n- Python_ (version 3.8 or later)\n- SciPy_\n- NumPy_\n\nOverview\n--------\n\nBoth the two-sided and the one-sided version of the test are supported. The former allows extracting outliers from both ends of the dataset, whereas the latter only considers min/max outliers. When running a test, every outlier will be removed until none can be found in the dataset. The output of the test is flexible enough to match several use cases. By default, the outlier-free data will be returned, but the test can also return the outliers themselves or their indices in the original dataset.\n\nExamples\n--------\n\n- Two-sided Grubbs test with a Pandas series input\n\n::\n\n   \u003e\u003e\u003e from outliers import smirnov_grubbs as grubbs\n   \u003e\u003e\u003e import pandas as pd\n   \u003e\u003e\u003e data = pd.Series([1, 8, 9, 10, 9])\n   \u003e\u003e\u003e grubbs.test(data, alpha=0.05)\n   1     8\n   2     9\n   3    10\n   4     9\n   dtype: int64\n   \n- Two-sided Grubbs test with a NumPy array input   \n\n::\n\n   \u003e\u003e\u003e import numpy as np\n   \u003e\u003e\u003e data = np.array([1, 8, 9, 10, 9])\n   \u003e\u003e\u003e grubbs.test(data, alpha=0.05)\n   array([ 8,  9, 10,  9])\n   \n- One-sided (min) test returning outlier indices\n\n::\n\n   \u003e\u003e\u003e grubbs.min_test_indices([8, 9, 10, 1, 9], alpha=0.05)\n   [3]\n   \n- One-sided (max) tests returning outliers\n\n::\n\n   \u003e\u003e\u003e grubbs.max_test_outliers([8, 9, 10, 1, 9], alpha=0.05)\n   []\n   \u003e\u003e\u003e grubbs.max_test_outliers([8, 9, 10, 50, 9], alpha=0.05)\n   [50]\n\n\n.. _Smirnov-Grubbs: https://en.wikipedia.org/wiki/Grubbs%27_test_for_outliers\n.. _SciPy: https://www.scipy.org/\n.. _NumPy: http://www.numpy.org/\n.. _Python: https://www.python.org/\n\n\nLicense\n=======\n\nThis software is licensed under the MIT License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fc-bata%2Foutlier-utils","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fc-bata%2Foutlier-utils","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fc-bata%2Foutlier-utils/lists"}