Metadata-Version: 2.1
Name: nishant_outlier_76
Version: 1.0.2
Summary: Outlier Removal Using Z-score or IQR
Home-page: https://github.com/nishu195/nishant_outlier_76
Author: Nishant Goel
Author-email: ngoel_be17@thapar.edu
License: UNKNOWN
Description: # Library for removing outliers from pandas dataframe
        
        ```
        PROJECT 2, UCS633 - Data Analysis and Visualization
        Nishant Goel  
        COE17
        Roll number: 101703376
        ```
        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 nishant_outlier_76`
        
        *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 nishant_outlier_76 import remove_outliers_iqr
        remove_outliers('input.csv', 'output.csv')
        ```
        ```
        from nishant_outlier_76 import remove_outliers
        remove_outliers('input.csv', 'output.csv',threshold)
        ```
        
Keywords: command-line,Outliers,outlier-removal,row-removal
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
