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Python package for Outlier Removal Algorithm using z_score or iqr.

Project description


Package Description :

Python package for Outlier Removal Algorithm using z_score or iqr.

Motivation :

This is a part of project - II made for UCS633 - Data analytics and visualization at TIET.
@Author : Sourav Kumar
@Roll no. : 101883068

Algorithm :

  • Z-SCORE : If the population mean and population standard deviation are known, the standard score of a raw score x is calculated as:
    z = (x - mean) / std.
    mean : is the mean of the sample.
    std : is the standard deviation of the sample.

  • Interquartile range : interquartile range (IQR), also called the midspread, middle 50%, or H‑spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles.
    IQR = Q3 − Q1
    The IQR of a set of values is calculated as the difference between the upper and lower quartiles, Q3 and Q1. Each quartile is a median calculated as follows :
    Given an even 2n or odd 2n+1 number of values.
    first quartile Q1 = median of the n smallest values
    third quartile Q3 = median of the n largest values
    The second quartile Q2 is the same as the ordinary median.

Getting started Locally :

Run On Terminal


Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.


Removes all the valid rows contaning outlier values from the dataset and prints the number of rows removed along with the columns which were considered for the algorithm.


  • The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.

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