101703311-OUTLIERS
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Readme
Project 2 : UCS633
Submitted By: Lokesh Arora 101703311
pypi: https://pypi.org/project/101703311_OUTLIERS/
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.
IQR = Q3 – Q1
MIN = Q1 - (1.5IQR)
MAX = Q3 + (1.5IQR)
Use the package manager pip to install 101703311_OUTLIERS.
pip install 101703311_OUTLIERS
101703311_OUTLIERS can be run as shown below:
>> outlierRemoval dataset.csv
| Marks | Students |
|---|---|
| 3 | Student1 |
| 57 | Student2 |
| 65 | Student3 |
| 98 | Student4 |
| 43 | Student5 |
| 44 | Student6 |
| 54 | Student7 |
| 99 | Student8 |
| 1 | Student9 |
| 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.
FAQs
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
We found that 101703311-OUTLIERS demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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