=============
outlier-utils
=============
Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs_ test.
Requirements
------------
- Python_ (version 3.8 or later)
- SciPy_
- NumPy_
Overview
--------
Both 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.
Examples
--------
- Two-sided Grubbs test with a Pandas series input
::
>>> from outliers import smirnov_grubbs as grubbs
>>> import pandas as pd
>>> data = pd.Series([1, 8, 9, 10, 9])
>>> grubbs.test(data, alpha=0.05)
1 8
2 9
3 10
4 9
dtype: int64
- Two-sided Grubbs test with a NumPy array input
::
>>> import numpy as np
>>> data = np.array([1, 8, 9, 10, 9])
>>> grubbs.test(data, alpha=0.05)
array([ 8, 9, 10, 9])
- One-sided (min) test returning outlier indices
::
>>> grubbs.min_test_indices([8, 9, 10, 1, 9], alpha=0.05)
[3]
- One-sided (max) tests returning outliers
::
>>> grubbs.max_test_outliers([8, 9, 10, 1, 9], alpha=0.05)
[]
>>> grubbs.max_test_outliers([8, 9, 10, 50, 9], alpha=0.05)
[50]
.. _Smirnov-Grubbs: https://en.wikipedia.org/wiki/Grubbs%27_test_for_outliers
.. _SciPy: https://www.scipy.org/
.. _NumPy: http://www.numpy.org/
.. _Python: https://www.python.org/
License
=======
This software is licensed under the MIT License.
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