Example usage

Here we will demonstrate how to use outliers to deal with the outliers in a dataset and plot the distribution of the dataset:

Imports

from outliers.outliers import outlier_identifier
from outliers.trim_outliers import trim_outliers
from outliers.visualize_outliers import visualize_outliers

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Input In [1], in <module>
----> 1 from outliers.outliers import outlier_identifier
      2 from outliers.trim_outliers import trim_outliers
      3 from outliers.visualize_outliers import visualize_outliers

ModuleNotFoundError: No module named 'outliers'

import pandas as pd
import altair as alt

Create a dataframe

We need to create a dataframe to work with.

df = pd.DataFrame({ 'SepalLengthCm' : [5.1, 4.9, 4.7, 5.5, 5.1, 50, 54, 5.0, 5.2, 5.3, 5.1],
                        'SepalWidthCm' :  [1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
                        'PetalWidthCm' :  [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5],
                        'class': ['Iris Setosa', 'Iris Versicolour', 'Iris Virginica', 'Iris Setosa', 'Iris Versicolour', 'Iris Virginica', 'Iris Virginica', 
                                'Iris Setosa', 'Iris Versicolour', 'Iris Setosa', 'Iris Versicolour']
})
df

	SepalLengthCm	SepalWidthCm	PetalWidthCm	class
0	5.1	1.4	0.2	Iris Setosa
1	4.9	1.4	0.2	Iris Versicolour
2	4.7	20.0	0.2	Iris Virginica
3	5.5	2.0	0.3	Iris Setosa
4	5.1	0.7	0.4	Iris Versicolour
5	50.0	1.6	0.5	Iris Virginica
6	54.0	1.2	0.5	Iris Virginica
7	5.0	1.4	0.6	Iris Setosa
8	5.2	1.8	0.4	Iris Versicolour
9	5.3	1.5	0.2	Iris Setosa
10	5.1	2.1	5.0	Iris Versicolour

Identify outliers

We can identify outliers using outlier_identifier. Note that this function will return a dataframe with the summary of the outlier identified by the method, with an additional column having if row has outlier or not if return_df = True.

outlier_identifier(df, columns=['SepalLengthCm', 'SepalWidthCm'], identifier = 'IQR', return_df=True)

	SepalLengthCm	SepalWidthCm	outlier
0	5.1	1.4	False
1	4.9	1.4	False
2	4.7	20.0	True
3	5.5	2.0	False
4	5.1	0.7	False
5	50.0	1.6	True
6	54.0	1.2	True
7	5.0	1.4	False
8	5.2	1.8	False
9	5.3	1.5	False
10	5.1	2.1	False

Trim outliers

We can trim outliers using trim_outliers. This function will return a dataframe which the outlier has already process by the chosen method.

trim_outliers(df, columns=['SepalLengthCm', 'SepalWidthCm', 'PetalWidthCm'],identifier='Z_score', method='trim')

	SepalLengthCm	SepalWidthCm	PetalWidthCm	class
0	5.1	1.4	0.2	Iris Setosa
1	4.9	1.4	0.2	Iris Versicolour
3	5.5	2.0	0.3	Iris Setosa
4	5.1	0.7	0.4	Iris Versicolour
5	50.0	1.6	0.5	Iris Virginica
6	54.0	1.2	0.5	Iris Virginica
7	5.0	1.4	0.6	Iris Setosa
8	5.2	1.8	0.4	Iris Versicolour
9	5.3	1.5	0.2	Iris Setosa
10	5.1	2.1	5.0	Iris Versicolour

Visualize outliers

We can trim outliers using visualize_outliers. This function will return an altair plot of data distribution with given method.

visualize_outliers(df, columns=None, type='violin')