semvec/pypi-packages huggingface dataset:
Topsis-Pranav-102103438 0.1.0
Topsis Package

# TOPSIS Python Implementation

## Introduction

This Python script performs the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis on a CSV file. It calculates the TOPSIS score and ranking for each alternative based on the provided weights and impacts.

## Installation

Ensure you have Python installed. You need to have the following packages installed:
-pandas
-numpy
-os

## Usage

from topsis_package import topsis_from_csv

Specify input and output file paths

input_file_path = 'input_data.csv'  # Replace with your input CSV file
output_file_path = 'output_data.csv'  # Replace with your desired output CSV file

Specify weights and impacts

weights = [0.25, 0.25, 0.25, 0.25]  # Adjust based on your criteria
impacts = ['+', '+', '+', '+']  # Adjust based on your criteria

 Perform TOPSIS and write results to CSV

topsis_from_csv(input_file_path, output_file_path, weights, impacts)

print(f"TOPSIS results have been written to '{output_file_path}'.")


## Input Format

The input csv file should contain n columns with n-1 critera where the first column refers to the names of the attributes

## Output Format

The output csv file contains the all the input columns along with two additional columns, namely, TOPSIS Score and Rank. The TOPSIS Score column contains the TOPSIS score for each alternative and the Rank column contains the rank of each alternative based on the TOPSIS score.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
topsis-aayushi-102103421 0.1.5
Topsis Implementation Package


## Overview

This is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.

## Installation

You can install the package using pip:

pip install topsis-aayushi-102103421

## USAGE
from topsis-aayushi-102103421 import topsis

#Example data (replace this with your actual data)

data = {
    'Alternative1': [1, 2, 3, 4],
    'Alternative2': [4, 3, 2, 1],
    # Add more alternatives and their values
}

#Criteria weights (replace this with your actual weights)

weights = [0.25, 0.25, 0.25, 0.25]

#Criteria impacts ('+' or '-' for each criterion)

impacts = ['+', '+', '+', '-']

#Perform TOPSIS analysis

result = topsis(data, weights, impacts)

#Display the ranking

print("Ranking:", result)


## Parameters
data: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.

weights: A list of weights corresponding to the importance of each criterion.

impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.

## License
This package is distributed under the MIT License - see the LICENSE file for details.
gitid 0.2.5
Command-line tool for managing multiple git identities on the same machine

# GitID

GitID is a command-line tool for managing multiple git identities on the same machine. This is particularly useful for shared machines where multiple people develop in the same workspaces, which is in some cases unavoidable.

With GitID, users can store their git identities and quickly load them in their shell, so their commits are marked with their name and email.

Note that GitID only sets commit authors and committers, and does **not** affect authorization.

## Installation

Installation is easy:
```bash
pip install gitid
gitid init bash # change to your shell of choice
```

### Installation Issues

Some environments may not place the script on the path. If your shell can't find the `gitid` script, you can do one of the following:

Install with sudo:
```bash
sudo -H pip install gitid
```

Or add the following snippet to your `~/.bashrc` (or equivalent startup file):
```bash
export PATH="$PATH:<PATH_TO_GITID>"
```

### Unsupported shell

If your shell is unsupported by `gitid init`, simply add `alias gitid="source gitid"` to the appropriate startup file of your shell.

## Workflow

Suppose both Frodo Baggins and Samwise Gamgee want to develop on the same computer. They would add their git identities like so:
```bash
gitid add frodo "Frodo Baggins" frodo@shire.com
gitid add sam "Samwise Gamgee" sam@shire.com
```

Now Frodo wants to write some code:
```bash
gitid set frodo
... git commands ...
git commit # This commit is marked with "Frodo Baggins <frodo@shire.com>" as the committer and author
```

Separately, possibly at the same time (in a different session), Samwise also wants to write code:
```bash
gitid set sam
... git commands ...
git commit # This commit is marked with "Samwise Gamgee <sam@shire.com>" as the committer and author
```

All identities can be viewed:
```bash
gitid list
```

Which outputs: (`*` marks the active identity)
```
Stored identities:
    ( ) frodo: Frodo Baggins <frodo@shire.com>
    (*) sam: Samwise Gamgee <sam@shire.com>
```

Identities can also be removed:
```bash
gitid remove frodo # removes Frodo's identity
gitid clear # removes all stored identities
```
hkapi 0.0.1
海康的python库
Requires Python: >=3.5

根据 海康 的综合安防管理平台写的python库

https://open.hikvision.com/docs/docId?productId=5c67f1e2f05948198c909700&version=%2Ff95e951cefc54578b523d1738f65f0a1&tagPath=%E7%BC%96%E7%A8%8B%E5%BC%95%E5%AF%BC-%E4%B8%80%E5%8D%A1%E9%80%9A%E5%BA%94%E7%94%A8%E6%9C%8D%E5%8A%A1
understat-package 0.3
A Python package to API Data understat.com/


<img src="https://github.com/lucbra21/images/blob/main/sdc_logo.jpeg" alt="Sports Data Campus" />

# API Data understat.com by Sports Data Campus

A Python package to api data understat.com

https://pypi.org/project/understat-package/

# Aviso Importante sobre el Uso de Datos de understat.com

Esta librería está diseñada para facilitar el acceso a los datos disponibles en [understat.com](https://understat.com/) con propósitos educativos y de investigación. Al utilizar esta librería, los usuarios deben adherirse a los siguientes términos y condiciones:

## Atribución de Datos
Todos los datos obtenidos a través de esta librería provienen de understat.com. Los usuarios deben asegurarse de atribuir claramente a understat.com como la fuente de los datos en cualquier publicación, análisis o proyecto donde se utilicen estos datos.

## Uso No Comercial
Esta librería se proporciona exclusivamente para uso no comercial. Cualquier uso de los datos obtenidos para propósitos comerciales está estrictamente prohibido.

## Adherencia a los Términos de Uso de understat.com
Se insta a los usuarios a revisar y seguir los términos de uso establecidos. El uso indebido de los datos puede resultar en la violación de estos términos.

## Limitación de Responsabilidad
El creador de esta librería no se hace responsable del mal uso de la misma ni de cualquier violación de los términos de uso de understat.com por parte de los usuarios. Los usuarios son responsables de asegurar que su uso de los datos cumple con todas las leyes y regulaciones aplicables.

Al utilizar esta librería, usted reconoce y acepta estos términos.


## Usage

```python

## Comprobar si understat-package esta instalado y su version
# !pip list

## Instalar
# !pip install understat_package

## Actualizar a la ultima version
# !pip install understat-package --upgrade

## Pre install
# !pip install sqlalchemy
# !pip install pandas mysql-connector-python

## Import package
import understat_package as understat

## Ejemplo de uso 1
# Obtenemos todos las competiciones
table_names = understat.getCompetitions()
table_names

## Ejemplo de uso 2
# Obtenemos la lista de equipos y tabla de posicion de cada liga
# df_result = understat.getLigas(season, copetition_id)
df_competition = understat.getLigas('2024', 'Serie A')
df_competition

## Ejemplo de uso 3
# Obtenemos los jugadores por temporada y competicion 
# df_result = understat.getJugadores_temporadas(season, copetition_id)
df_result = getJugadores_temporadas('2024','Serie A')
df_result

## Ejemplo de uso 4
# Obtenemos los disparos por temporada y competicion
# df_result = understat.getDisparos(season, copetition_id)
df_result = understat.getDisparos('2024','Serie A')
df_result

## Ejemplo de uso 5
# Obtenemos la lista de partidos de cada competicion y temporada
# df_result = understat.getPartidos(season, copetition_id)
df_result = understat.getPartidos('2024','Serie A')
df_result


```

by Sports Data Campus
time-app 1.0
Описание
Requires Python: >=3.6

# Библиотека `time_app`

## Описание

`time_app` – это удобная библиотека для работы с временем и датой в языке программирования Python. Она предоставляет простые и мощные инструменты для управления временными данными, облегчая выполнение операций, таких как форматирование, разбор, сравнение и арифметика с датами и временем.

## Основные возможности

1. **Форматирование и разбор даты/времени:**
   - Преобразование даты и времени в строку по заданному формату.
   - Парсинг строки с датой/временем в объект Python `datetime`.

2. **Арифметика с датами и временем:**
   - Удобные методы для добавления и вычитания времени, дней, месяцев и лет.
   - Расчет разницы между двумя датами.

3. **Сравнение дат и времени:**
   - Сравнение дат и времени на равенство, больше/меньше.
   - Проверка, находится ли текущая дата/время в указанном временном диапазоне.

4. **Работа с таймзонами:**
   - Поддержка работы с различными временными зонами.
   - Преобразование времени между разными таймзонами.

5. **Управление временными интервалами:**
   - Работа с интервалами времени.
   - Поддержка периодических задач и планирования событий.

## Пример использования

```python
from time_app import TimeApp

# Создание объекта с текущей датой и временем
now = TimeApp.now()

# Форматирование даты в строку
formatted_date = now.format("%Y-%m-%d %H:%M:%S")
print(f"Форматированная дата: {formatted_date}")

# Арифметика с датами
future_date = now + TimeApp.timedelta(days=7)
print(f"Дата через 7 дней: {future_date}")

# Проверка, является ли текущая дата позже указанной
if now > future_date:
    print("Текущая дата находится позади будущей даты.")
else:
    print("Текущая дата находится впереди будущей даты.")