semvec/pypi-packages huggingface dataset:
rahul-pattern 1.0.0
This is a pattern printing program
Requires Python: >=3.7

# rahul_pattern

## A simple python package for pattern printing

---

# Use the package in your project

### Install the package

```
pip install rahul_pattern
```

### Using the package

```
import rahul_pattern.pattern as pattern

pattern.pattern_triangle(5)
```

---

Rahul Pain © 2023

> **Note**
> This is for a university project. Might not be maintained in future.
topsis-Suddhasattwa-Khan-102003687 0.0.0
A python package to implement topsis
Requires Python: >=3.6
Keywords: topsis_suddhasattwa_khan_102003687,topsis-suddhasattwa-102003687,topsis,Maths

# Topsis implemented by Suddhasattwa Khan 102003687

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

## Functionality of the Package

- Outputs the result(topsis score) and ranking of items in output.csv file you will mention in terminal or command promt
- Inputs the file path of input csv file in terminal

## Example

```
#test.py
##
type:
from topsis_suddhasattwa_khan_102003687 import topsis
n=len(sys.argv)
inputFile= sys.argv[1]
weight=sys.argv[2]
impact=sys.argv[3]
outputFile=sys.argv[4]
# topsis(inputFile, weight, impact, outputFile)
```

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.

```
 pip install topsis_suddhasattwa_khan_102003687
```

## Run the following Script.

```
 python setup.py <inputfile.csv> weight impact <outputfile.csv>
```

## Note

- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.
Tsong 0.0.1
simple program that allows you to play music on a linux terminal

# Tsong

Tsong is a simple program that allows you to play, pause, stop, skip, and adjust the volume of audio files in a specified folder. It uses the Pygame library to handle the audio playback and the os module to interact with the file system.

## Getting Started

- Clone the repository to your local machine.

- Make sure you have Pygame library installed. If not, you can install it by running pip install pygame

- Run the script by running python Tsong.py

- When prompted, enter the location of the folder containing the audio files you want to play. The program will only play mp3 files.

Once the first track has been loaded and started playing, you can use the following commands to control the playback:

play: resume playback
pause: pause playback
stop: stop playback and exit the program
next: skip to the next track
previous: go back to the previous track
current: display the name of the current track
skip: skip to a specific time (in milliseconds)
volume up: increase the volume
volume down: decrease the volume

## License

This project is licensed under the MIT License - see the LICENSE file for details.

### Additional help ##

Please let me know if you need more help or have any questions about this program.
topsis-Ananya-102017024 1.0.0
This program uses command line arguments to implement Topsis : a Multi-Criteria Decision Ananlysis (MCDA) method
Requires Python: >=3.5
Keywords: python,TOPSIS,MCDA,MCDM,predictive analytics,statistics

# Project Description
## Topsis

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a Multi-Criteria Decision Ananlysis (MCDA) method that is used to solve multiple criteria decision making problems.

It uses the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one and ranks solutions that have multiple criterias.

## Installation
Use ```pip``` to install:
```
pip install topsis-ananya-102017024
```
**Prerequisite**
Libraries needed to be downloaded before running this package are :
- Numpy
- Pandas

## Usage
Run ```topsis``` in the input file's directory as follows:
```
topsis <input_file_name> <weights> <impacts> <output_file_name>
```
For example,
Use quotation marks while including spaces in any argument:
```
topsis data.csv "1, 1, 1, 1" "+, -, +, -" result.csv
```
or vectors can be entered without any space
```
topsis data.csv 1,1,1,1 +,-,+,- result.csv
```

##### Note that
- Input file must contain three or more columns.
- Except the first column of the input file the rest must be numeric
- Weights must be numeric
- Impacts must be either 
  - ```+``` (for features that are to be maximised) 
  - ```-``` (for features that are to be minimized)
- Both Impacts & Weights must be seperated by a comma ```,``` 

## Example
**input.csv**
| Attribute | Price | Storage | Camera | Looks|
| ------ | ------ | ------ | ------ | ------ |
| M1 | 250 | 16 | 12 | 5 |
| M2 | 200 | 16 | 8 | 3 |
| M3 | 300 | 32 | 16 | 4 |
| M4 | 275 | 32 | 8 | 4 |
| M5 | 225 | 16 | 16 | 2 |

On running the following command
``` 
topsis input.csv "0.25,0.25,0.25,0.25" "-,+,+,+" output.csv
```

**output.csv**
| Attribute | Price | Storage | Camera | Looks| Topsis Score | Rank |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| M1 | 250 | 16 | 12 | 5 | 0.534269 | 3 |
| M2 | 200 | 16 | 8 | 3 | 0.308314 | 5 |
| M3 | 300 | 32 | 16 | 4 | 0.691686 | 1 |
| M4 | 275 | 32 | 8 | 4 | 0.534807 | 2 |
| M5 | 225 | 16 | 16 | 2 | 0.401222 | 4 |


## License

MIT
Suddhasattwa-Khan-102003687 0.0.1
A python package to implement topsis
Requires Python: >=3.6
Keywords: topsis_suddhasattwa_khan_102003687,topsis-suddhasattwa-102003687,topsis,Maths

# Topsis implemented by Suddhasattwa Khan 102003687

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

## Functionality of the Package

- Outputs the result(topsis score) and ranking of items in output.csv file you will mention in terminal or command promt
- Inputs the file path of input csv file in terminal

## Example

```
#test.py
##
type:
from topsis_suddhasattwa_khan_102003687 import topsis
n=len(sys.argv)
inputFile= sys.argv[1]
weight=sys.argv[2]
impact=sys.argv[3]
outputFile=sys.argv[4]
# topsis(inputFile, weight, impact, outputFile)
```

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.

```
 pip install topsis_suddhasattwa_khan_102003687
```

## Run the following Script.

```
 python setup.py <inputfile.csv> weight impact <outputfile.csv>
```

## Note

- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.
Topsis-HarshPaba-102003675 0.0.1
A python package to implement topsis
Requires Python: >=3.6
Keywords: topsis,maths,multiple criteria decision making

# Topsis implemented by Harsh Paba

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)   

## Functionality of the package

- Outputs the performance score and rank corresponding to each row of the dataset.

## Usage

- Make sure you have Python installed in your system.
- Run the following command in terminal.
 ```
  pip install Topsis_HarshPaba_102003675
  ```
## Example
  ```
  from Topsis_HarshPaba_102003675 import find_rank
  ip=sys.argv[1]
  wt=sys.argv[2]
  im=sys.argv[3]
  op=sys.argv[4]
  find_rank(ip,wt,im,op)
  ```

## Run the following Script.
 ```
  python test.py <inputFile.csv> weights impacts <outputFile.csv>
Topsis-Shubham-102067011 0.0.4
TOPSIS for MCDM(Multiple Criteria Decision Making)
Keywords: TOPSIS

Library used to calculate topsis score 

TOPSIS is used for multiple Criteria Decision Making (MCDM)

TOPSIS is based on the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one. Alternatives are ranked with the use of an overall index calculated based on the distances from the ideal solutions.

---------------------

normalize function takes a dataframe as argument .

It normalizes columns 2nd to the last column of the dataframe.

All the values from the 2nd column to the last column should be numeric.

Outputs a dataframe.

---------------------

weightAssignment takes a dataframe(output of normalize function) and a numeric list as inputs. 

weightAssignment function assigns weights to different columns .

Outputs a dataframe.

---------------------

ideal function takes a dataframe(output of weightAssignment function) and a list of string having elements either '+' or '-'.

Used to calculate max and minimum value of each column.

Outputs a pair of numeric lists.

----------------------

euclideanDist function takes a dataframe(output of weightAssignment function) and 2 numeric lists(output of ideal function).

Calculates the euclidean distance of each row of dataframe with the 2 numeric lists.

Outputs a pair of numeric lists.

----------------------

performance_score takes 2 numeric lists (output of euclideanDist function) as inputs.

Calculates topsis score of each row.

Outputs a numeric list.

----------------------

TOPSIS_result takes a dataframe(output of weightAssignment function or Orignal dataframe) and a numeric list(output of performance_score function) as inputs.

assigns a rank to the list.

The ranks and the list are appended to the dataframe.

Outputs a dataframe.
-----------------------

Change Log
==============

0.0.1 (21/01/2023)
---------------------
- First Release
Topsis-102003592 0.3
Evaluation of alternatives based on multiple criteria using TOPSIS method.

## Topsis_102003592

# TOPSIS - Multiple Criteria Decision Making

Submitted By: **Bhavna Goyal - 102003592**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **0.3**.

Date: **21-01-2023**.

Author: **Bhavna Goyal**.

Maintainer: **Bhavna Goyal <bgoyal_be20@thapar.edu>**.

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_102003592
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" 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.

| Attribute | Price or cost | Storage Space | Camera| Looks |
| -----     | -----------   | ------------- | ---- | -------|
| Mobile 1  |250$           | 16GB          | 12MP | 5      |
| Mobile 2  | 200$          | 16GB          | 8MP  | 3      |
| Mobile 3  | 300$          | 32GB          | 16MP | 4      |
| Mobile 4  |  275$         | 32GB          | 8MP  | 4      |
| Mobile 5  | 225$          | 16GB          | 16MP | 2      |

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)

| Attribute | Price or cost | Storage Space | Camera| Looks | Topsis_score | Rank|
| -----     | -----------   | ------------- | ---- | -------| ------------ | ----|
| Mobile 1  |250$           | 16GB          | 12MP | 5      | 0.4228       | 4   |
| Mobile 2  |200$           | 16GB          | 8MP  | 3      | 0.4635       | 3   |
| Mobile 3  |300$           | 32GB          | 16MP | 4      | 0.5097       | 2   |
| Mobile 4  |275$           | 32GB          | 8MP  | 4      | 0.3772       | 5   |
| Mobile 5  |225$           | 16GB          | 16MP | 2      | 0.6871       | 1   |


<br>
The output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*
shinytext 0.0.3
A small text processing package
Requires Python: >=3.10

# ✨ Shiny Text
This package aims to be a one stop all package for all text processing tasks.
The package was developed using Python3.10.

### 💻 Installation
```python
pip install -U shinytext
```

### 🚀 Usage
```python

# To clean text
from shinytext.cleantext import <method>

# To clean dataframe
from shinytext.cleandata import <method>

# To download/fetch a spacy language model
from shinytext.utils.spacy_loader import get_spacy_object
```
#### List of text cleaning methods

* **remove_punctuation**: To remove punctuations
* **remove_stopwords**: To remove stopwords
* **remove_frequent_words**: To remove frequently occuring words
* **remove_rare_words**: To remove rarely occuring words
* **stemmer**: To perform stemming
* **lemmatizer**: To perform lemmatizing
* **remove_emojis**: To remove emoji's
* **convert_emojis**: To convert emoji's to text
* **remove_emoticons**: To remove emoticons
* **convert_emoticons**: To convert emoticons to text
* **remove_urls**: To remove URLs
* **remove_html**: To remove html
* **convert_chatwords**: To convert chat words to text

#### List of dataframe cleaning methods

* **filter_english_records**: To extract data with only english text


### ✍ Contribution Guidelines
Please ensure that you adhere to the following guidelines while making a pull request.

1. Fork the project on to your repo. Create a branch under your name and create PR from your branch.

2. USE proper commit messages and give detailed descriptions for your commit including the file name, function name and the changes made.

Customize your commit titles according to below given instructions  

    * "ADD: <your task title>" for new additions  
    * "MOD: <your task title>" for modifications 
    * "DEL: <your task title>" for deletions
    * "FIX: <your task title>" for fixing bugs

3. Ensure that your code adheres to PEP-8 guidelines. [Click here](https://peps.python.org/pep-0008/) to know more about it.

### 💼 Current Requirements
1. More text processing techniques

2. Test cases for existing functions

### 🙌 Contributors
1. [Retin P Kumar](https://github.com/Retinpkumar)

### ❤ Credits
1. [Emot library](https://github.com/NeelShah18/emot) by [Neel Shah](https://github.com/NeelShah18)
2. [Getting started with Text Preprocessing](https://www.kaggle.com/code/sudalairajkumar/getting-started-with-text-preprocessing/notebook) by [Sudalai Rajkumar](https://www.kaggle.com/sudalairajkumar)

### ⚙ Dependancies
* [NLTK](https://www.nltk.org/)
* [Pandas](https://pandas.pydata.org/)
* [PySpellchecker](https://pypi.org/project/pyspellchecker/)
* [SpaCy](https://spacy.io/)