Outlier Removal Using Z-score or IQR
Project description
Library for removing outliers from pandas dataframe
PROJECT 2, UCS633 - Data Analysis and Visualization
Paras Arora
COE18
Roll number: 101703382
Takes two inputs - filename of input csv, intended filename of output csv.
Output is the number of rows removed from the input dataset.It also shows new dataset in case of IQR
Output is the number of rows removed from the input dataset in case of z-score
Installation
pip install outlier_101703382
Recommended - test in a virtual environment.
Use via command line
outliers_cli in.csv out.csv
outliers_cli in.csv out.csv 1.5
First argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.
Use in .py script
from outlier_101703382 import remove_outliers_iqr
remove_outliers('input.csv', 'output.csv')
from outlier_101703382 import remove_outliers
remove_outliers('input.csv', 'output.csv',threshold)
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
Close
Hashes for outlier_101703382-1.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a4f0f51e3382e83bba6063c37bf19f46123c7d0d4dc44d30a400923974d54d11 |
|
MD5 | 3fbb08d0e0187ecdaf05a84bc15a809d |
|
BLAKE2b-256 | 1d603bde7706bd111f5f398650cb1e68f886b32aa77f711ea93a743d25797032 |