Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.

• Python (version 3.8 or later)
• SciPy
• NumPy

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.

• 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]

This software is licensed under the MIT License.