A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Project description
Outlier Removal Using InterQuartile Range
Project 2 : UCS633
Submitted By: Lovish Jindal 101703312
pypi: https://pypi.org/project/101703312_outlierRemoval/
InterQuartile Range (IQR) Description
Any set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:
The minimum or lowest value of the dataset.
The first quartile Q1, which represents a quarter of the way through the list of all data.
The median of the data set, which represents the midpoint of the whole list of data.
The third quartile Q3, which represents three-quarters of the way through the list of all data.
The maximum or highest value of the data set.
These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.
Calculation of IQR
IQR = Q3 – Q1
MIN = Q1 - (1.5IQR)
MAX = Q3 + (1.5IQR)
Installation
Use the package manager pip to install 101703312_outlierRemoval.
pip install 101703312_outlierRemoval
How to use this package:
101703312_outlierRemoval can be run as shown below:
In Command Prompt
>> outlierRemoval dataset.csv
Sample dataset
Marks | Students |
---|---|
3 | Student1 |
57 | Student2 |
65 | Student3 |
98 | Student4 |
43 | Student5 |
44 | Student6 |
54 | Student7 |
99 | Student8 |
1 | Student9 |
Output Dataset after Removal
Marks | Students |
---|---|
57 | Student2 |
65 | Student3 |
98 | Student4 |
43 | Student5 |
44 | Student6 |
54 | Student7 |
It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.
License
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for 101703312_outlierRemoval-1.0.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 24b70178f30ea872ddc3e04d574ded096829065f76161d41d3aab0584a2cb15c |
|
MD5 | c66e7d29829796408f2cbf02e8b0e5b7 |
|
BLAKE2b-256 | 233575d5da6a6842b36aeaccbd4ab591efd8e2ff8ba2c57dd799c99552ba4f06 |
Hashes for 101703312_outlierRemoval-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c70bc8c6e441925a7903f15ca74153922423a7ad4c68be355f9e356dc5321ac0 |
|
MD5 | 86a4af69aa052467105888382856f36e |
|
BLAKE2b-256 | c81cb42bb9263502a18612b0476fd3312870c1740e153cecada5d65948e5cee5 |