Python package for Outlier Removal Algorithm using z_score or iqr.
Project description
outlier-python
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 In IDLE
Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.
OUTPUT :
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.
TESTING :
- 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.
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 outlier-python-souravdlboy-0.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0c26b9d6df9996aa66e1da5608c7d893eb58641538689f7e285c18c23ee37dd8 |
|
MD5 | 080225043f6ca9e63e3099c6f143eb38 |
|
BLAKE2b-256 | b5526e997bd3bd5dbe4ba636a798fdca973fd586b4a8104255c7ffa2411a90f0 |
Hashes for outlier_python_souravdlboy-0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ccf1de133f447d6d77bba819bf275f39262459b2796cb61e2f9962146b057d35 |
|
MD5 | eb89426a8a165ea2347215c3519ab41e |
|
BLAKE2b-256 | 402af8bf1bf0d23d19589edd0e2ca21bb71e0b727aa95f46d57f90a4a90ee6da |