semvec/pypi-packages huggingface dataset:
Div-square 1.0.0
It squares the number
License: MIT

# Project description
Div-square
It takes an integer as an input and prints it square.

## Installation
```pip install saral-square```

## How to use it?
Open terminal and type square and then input the integer

## License
Â© 2022 Divya Goel

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-Preetinder-101903403 0.0.1
Topsis impelementation in python
License: MIT
Keywords: Topsis

This library is used to implement topsis on CSV files in python.
Topsis is used for multiple criteria decision making
The syntax for using this library is-

pip install Topsis-Preetinder-101903403
python3
>>>t = topsis.topsis([filename], [weights], [impacts], [output_filename])
e.g. t = topsis.topsis("data.csv","1,2,1,1","-,+,+,+", "output.csv")

Change Log
=============

0.0.1 (26/02/2022)
-------------
- First Release
pyggui 0.0.1
Pygame project for simplifying page and menu creation while still leaving full controll
License: MIT
Requires Python: >=3.6

========
Overview
========



A Python gui library based around Pygame, meant for simplifying gui development for menus on simple pixel art games (or other types of games). 
Designed for customization and simplicity. Although the library comes with pre-designed gui items, each can be customized and its functionality personalized to fit your needs.

Start with creating the main Game object, specify your global settings (such as screen size, FPS cap, ...), run the main game loop from it. Create your custom page, add items to it, custom event handlers and more. It will be auto-imported and loaded into your game. 

Your game needs its own loop for optimization reasons? No problem, create a dummy page and run your loop from there. Keep page, menu and item logic separated and tidy. 

See https://python-pyggui.readthedocs.io/en/latest/usage.html for basic concepts.

* Free software: MIT license

Installation
============

::

    pip install pyggui

You can also install the in-development version with::

    pip install https://github.com/15minutOdmora/python-pyggui/archive/main.zip


Documentation
=============


https://python-pyggui.readthedocs.io/


Development
===========

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox


Changelog
=========

0.0.0 (2021-12-24)
------------------

* First release on PyPI.
Topsis-Bhavya-101903365 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: **Bhavya Kakwani**

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

The Topsis-Bhavya-101903365 has a function topsis() that takes 4 parameters, i.e., inputFile, weights, impacts, and outputFile, and returns the resulting dataframe having **Topsis Score** and **Rank** as additional columns. 

Where,

* **inputFile:** Can be either csv file or pandas dataframe. Input file must contain three or more columns, where, First column is the object/variable name like M1, M2, M3, M4, etc. Also, from 2nd to last columns must contain numeric values only.
* **weights:** In the form of string having numerical values separated by commas.
* **impacts:** In the form of string having + or - values separated by commas. Here, + refers to positive impact, whereas, - refers to negative impact.
* **outputFile** (optional): csv file in which output of the function will be stored.


For Example,

### Method 1: By passing csv file as input
```python
>>> import Topsis_Bhavya_101903365 as t
>>> inputFile = "input.csv"
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = t.topsis(inputFile, weights, impacts)
```
<br>

### Method 2: By passing pandas dataframe as input
```python
>>> import Topsis_Bhavya_101903365 as t
>>> import pandas as pd
>>> dataFrame = pd.read_csv("input.csv)
>>> weights = "1,1,1,2,1"
>>> impacts = "+,+,-,+,+"
>>> result_df = t.topsis(dataFrame, weights, impacts)
```

<br>

## Sample Input 

### Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
----------|----|----|----|----|----
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05

### Weights

weights = "1,1,1,2,1"

### Impacts

impacts = "+,+,-,+,+"

<br>

## Sample Output

Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank
----------|----|----|----|----|----|--------------|------
M1 | 0.65 | 0.42 | 3.3 | 46.3 | 12.67 | 0.41202513 | 7
M2 | 0.81 | 0.66 | 4.9 | 51.4 | 14.44 | 0.510060544 | 2
M3 | 0.87 | 0.76 | 6 | 65.4 | 18.26 | 0.685105262 | 1
M4 | 0.87 | 0.76 | 4.2 | 40.7 | 11.63 | 0.433129944 | 5
M5 | 0.75 | 0.56 | 6.8 | 57.5 | 16.4 | 0.469643489 | 3
M6 | 0.64 | 0.41 | 5.3 | 44.7 | 12.76 | 0.225789842 | 8
M7 | 0.77 | 0.59 | 4.7 | 49.8 | 13.97 | 0.451566364 | 4
M8 | 0.7 | 0.49 | 3.1 | 43.9 | 12.05 | 0.418005937 | 6 

<br>
topsis-banaj-101916008 1.1.1
It's a package that calcuates Topsis score and ranks accordingly
License: MIT

# TOPSIS

Submitted By: **Banaj Bedi**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.1.1**.
<br>
Date: **2022-02-26**.
<br>
Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install topsis_banaj_101916008
```

### In Command Prompt

```
>> topsis data.csv "1,1,2,1,2" "+,+,-,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

![image](https://user-images.githubusercontent.com/83486603/155836677-32ab8148-315c-42ff-b949-795dc881c059.png)



Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

![image](https://user-images.githubusercontent.com/83486603/155836697-80af34db-ea1c-4deb-9bee-4e6c6be16cb4.png)


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**

View package at: https://pypi.org/project/topsis-banaj-101916008/