=============
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.