semvec/pypi-packages huggingface dataset:
Gourang 1.0.0
A simple Python package
Requires Python: >=3.6

# `Gourang`

The `Gourang` is a simple testing example to understand the basics of developing your first Python package.
MIT License

Copyright (c) [2023] [Gourang Agrawal]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Topsis-Gurpreet-102003070 1.0.0
Topsis package for MCDM problems
Requires Python: >=3.6

# Topsis-Gurpreet-102003070
For : Assignment(UCS654)<br>
Submitted by: Gurpreet Singh<br>
Roll no:102003070
Group:3COE3
## Description
This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)

<br>

## How to use this package:

## Installation

pip install Topsis-Gurpreet-102003070

## Example:

## Sample dataset
Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37

## Input
### In Command Prompt
Enter filename followed by .csv extension, then enter values of weights separated by commas like "1,1,1,2,2",then enter values of impacts separated by commas like "+,+,-,-,+" without giving space in between comma value, then enter name of file where you want to save output followed by .csv extension
```
topsis data.csv "1,1,1,2,2" "+,+,-,-,+" out.csv
```
## Output
This will be in our Output csv file
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2
Topsis-Gurpreet-check 1.0.0
Topsis package for MCDM problems
Requires Python: >=3.6

# Topsis-Gurpreet-check
## Description
This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)

<br>

## How to use this package:

## Installation

pip install Topsis-Gurpreet-check

## Example:

## Sample dataset
Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37

## Input
### In Command Prompt
Enter filename followed by .csv extension, then enter values of weights separated by commas like "1,1,1,2,2",then enter values of impacts separated by commas like "+,+,-,-,+" without giving space in between comma value, then enter name of file where you want to save output followed by .csv extension
```
topsis data.csv "1,1,1,2,2" "+,+,-,-,+" out.csv
```
## Output
This will be in our Output csv file
Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2