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        "description": "=============\noutlier-utils\n=============\n\n.. image:: https://travis-ci.org/c-bata/outlier-utils.svg?branch=master\n    :target: https://travis-ci.org/c-bata/outlier-utils\n\nUtility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs_ test.\n\nRequirements\n------------\n\n- Python_ (version 2.7, 3.4 and 3.5)\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   >>> from outliers import smirnov_grubbs as grubbs\n   >>> import pandas as pd\n   >>> data = pd.Series([1, 8, 9, 10, 9])\n   >>> 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   >>> import numpy as np\n   >>> data = np.array([1, 8, 9, 10, 9])\n   >>> 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   >>> 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   >>> grubbs.max_test_outliers([8, 9, 10, 1, 9], alpha=0.05)\n   []\n   >>> 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\n\nCHANGES\n=======\n\n0.0.3 (2016-04-25)\n------------------\n\nThanks to `@lukius <https://github.com/lukius>`_ .\n\n- Support for one-sided (min/max) tests.\n- Test output is now more flexible: the user can run the test in order to find the outliers themselves or the indices of the outliers, and not just the outlier-free data.\n- Test suite was enhanced.\n- README was extended and improved.\n- Japanese comments were translated to English so as to reach a greater audience.\n\n0.0.2 (2015-12-02)\n------------------\n\nUpdate setup.py\n\n0.0.1 (2015-12-01)\n------------------\n\nPublish to pypi\n\n0.0.0 (2015-07-28)\n------------------\n\nCreate this project.",
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