semvec/pypi-packages huggingface dataset:
topsis-navkiran 1.1.4
TOPSIS implementation in python for multi-criteria decision making
Requires Python: >=3.6
Keywords: command-line,topsis-python,TOPSIS

# TOPSIS implementation in python for multi-criteria decision making

```
Class project for DATA ANALYSIS AND VISUALISATION 2020 - UCS633 Thapar University, Patiala
Navkiran Singh  

COE17
Roll number: 101703365
```

![](/../screenshots/1.png?raw=true "Optional Title")
## Update: Added windows command line support

Output is a dataframe with 3 columns
 - **Alternatives** serial number
 - Corresponding performance **Score** or closeness to ideal solution
 - **Rank**

## Installation
`pip install topsis_navkiran`

*Note the name has an underscore not a hyphen. If installation gives error or package is not found after installing, install as sudo.*

*Recommended - test it out in a virtual environment.* 

## Upgrade
`pip install topsis_navkiran --upgrade`

## To use via command line
`topsis_navkiran_cli data.csv 25,25,25,25 -+++`

First argument after run.py is filename with .csv extension. The .csv file is assumed to have a structure similar to one provided in topsis_navkiran/data.csv

That is, the .csv file should have a header with column names and first column should only list alternatives and not attribute values.

## To use in .py script
```
from topsis_navkiran import topsis
"""
decision_matrix is 2D numpy array, weights is a 1D array and impacts is a string of the form +-+-- 
where + indicates benefit and - indicates cost
"""
output_dataframe = topsis(decision_matrix,weights,impacts)
```
## Debugging and Exception Handling
> The program has several assert statements which raise errors with helpful description in the following cases:
> - Wrong dimensions of decision matrix (*not* 2D), weights (*not* 1D)
> - Length of weights and impacts don't match 
> - Weights or impacts don't match number of attributes
> - For command line, number of arguments is less than 3 required
> - File extension must be .csv

*Based off on a similar package for TOPSIS in R*
outliers-navkiran 1.1.0
Outlier Removal Using Z-score or IQR
Requires Python: >=3.6
Keywords: command-line,Outliers,outlier-removal,row-removal

# Library for removing outliers from pandas dataframe

```
PROJECT 2, UCS633 - Data Analysis and Visualization
Navkiran Singh  
COE17
Roll number: 101703365
```
## Update in 1.1.0 - command line script method changed, supports calling from both windows and linux terminal 
Takes two inputs - filename of input csv, intended filename of output csv. 

Two optional arguments - which must be provided together or left out.
Third argument is threshold, by default it's 1.5. Fourth argument is method - z_score or IQR.

Output is the number of rows removed from the input dataset. Resulting csv is saved as output.csv. 


## Installation
`pip install outliers_navkiran`

*Recommended - test in a virtual environment.* 

## Use via command line
`outliers_navkiran_cli in.csv out.csv`

Defaults are 1.5 threshold and IQR.

When providing custom threshold and method:

`outliers_navkiran_cli in.csv out.csv 3 z_score`
`outliers_navkiran_cli in.csv out.csv 3 IQR`

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 outliers_navkiran import remove_outliers_z,remove_outliers_iqr
# for using z-score
remove_outliers_z('input.csv', 'output.csv',1.5)
# for using IQR
remove_outliers_iqr('input.csv', 'output.csv',1.5)
```