py_outliers_utils.trim_outliers

Module Contents

Functions

trim_outliers(dataframe, columns=None, identifier='IQR', method='trim')

A function to generate outlier free dataset by imputing them with mean, median or trim entire row with outlier from dataset.
py_outliers_utils.trim_outliers.trim_outliers(dataframe, columns=None, identifier='IQR', method='trim')[source]
A function to generate outlier free dataset by imputing them with mean, median or trim entire row with outlier from dataset.
dataframepandas.core.frame.DataFrame

The target dataframe where the function is performed.

columnslist, default=None

The target columns where the function needed to be performed. Default is None, the function will check all columns

identifierstring

The method of identifying outliers. - if “Z_score” : Use z-test with threshold of 3 - if “IQR” : Use IQR (Inter Quantile range) to identify outliers

methodstring
The method of dealing with outliers.

  • if “trim” : remove completely rows with data points having outliers.

  • if “median” : replace outliers with median values

  • if “mean” : replace outliers with mean values

Returns

a dataframe which the outlier has already process by the chosen method.

Return type

pandas.core.frame.DataFrame

Examples

>>> import pandas as pd

>>> df = pd.DataFrame({
>>>    'SepalLengthCm' : [5.1, 4.9, 4.7, 5.5, 5.1, 50, 5.4, 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]
>>> })
>>> trim_outliers(df, columns=['SepalLengthCm', 'SepalWidthCm', 'PetalWidthCm'],identifier='Z_score', method='trim')
     SepalLengthCm          SepalWidthCm       PetalWidthCm
0   5.1                     1.4                     0.2
1   4.9                     1.4                     0.2
2   5.5                     2.0                     0.3
3   5.1                     0.7                     0.4
4   5.4                     1.2                     0.5
5   5.0                     1.4                     0.6
6   5.2                     1.8                     0.4
7   5.3                     1.5                     0.2