semvec/pypi-packages huggingface dataset:
Topsis-SnehithaM-101917176 0.0.2
Compute Topsis Scores/Ranks of a given csv file
License: MIT
Keywords: TOPSIS

What is TOPSIS? 
 It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.Topsis Value CalculatorThis Python package implementing Topsis method  for multi-criteria decision analysis. 
 Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution 
  Just provide your input attributes and it will give you the results 
 Installation: $ pip install TOPSIS-101917176==0.0.1 
 In the commandline, you can write as - $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name> 
 E.g for input data file as data.csv, command will be like $ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv 
 This will print all the output attribute values along with the Rank column, in a tabular format
Topsis-Disha-101903291 0.0.6
Get topsis score
License: MIT

# Topsis_Disha

Topsis_Disha is a library for calculating topsis score and ranking them according to the score.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.

```bash
pip install Topsis-Disha-101903291
```

## Code Snippet

```python
from topsis import main
main()
```

## Usage in terminal

```python
python <filename.py> <data.csv> '1,1,1,1,1' '+,+,-,+,+' <result.csv>
```

## Input file (data.csv)

In this script, the first column should be named 'Fund Name' for the script to work correctly

| Fund Name | P1   | P2   | P3   | P4    |
| --------- | ---- | ---- | ---- | ----- |
| M1        | 0.79 | 0.62 | 1.25 | 60.89 |
| M2        | 0.66 | 0.44 | 2.89 | 63.07 |
| M3        | 0.56 | 0.31 | 1.57 | 62.87 |
| M4        | 0.82 | 0.67 | 2.68 | 70.19 |
| M5        | 0.75 | 0.56 | 1.3  | 80.39 |

## Output file (result.csv)

| Fund Name | P1   | P2   | P3   | P4    | Topsis Score | Rank |
| --------- | ---- | ---- | ---- | ----- | ------------ | ---- |
| M1        | 0.79 | 0.62 | 1.25 | 60.89 | 0.7722       | 2    |
| M2        | 0.66 | 0.44 | 2.89 | 63.07 | 0.2255       | 5    |
| M3        | 0.56 | 0.31 | 1.57 | 62.87 | 0.4388       | 4    |
| M4        | 0.82 | 0.67 | 2.68 | 70.19 | 0.5238       | 3    |
| M5        | 0.75 | 0.56 | 1.3  | 80.39 | 0.8113       | 1    |

The output file contains columns of input file along with two additional columns having **Topsis Score** and **Rank**