semvec/pypi-packages huggingface dataset:
ExpressLibrary 0.1.0
Express library is a Python package designed to streamline the data visualization process in a consultancy environment. This package aims to facilitate the quick transformation of data into insightful visualizations. It is developed on top of standard libraries such as Plotly and Circlify.

# README.md for ExpressLibrary

## Table of Contents

- [Introduction](#introduction)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Introduction

`ExpressLibrary` is a Python package designed to streamline the data visualization process in a consultancy environment. This package aims to facilitate the quick transformation of data into insightful visualizations. It is developed on top of standard libraries such as Plotly and Circlify and offers easy integration with Power BI.

## Installation

```bash
pip install ExpressLibrary
```

## Usage

```python
from ExpressLibrary import BarChart

# Load your data (as pandas DataFrame)
data = ...

# Create chart
chart = BarChart(data, 'x_column_name', ['y_column_name'])
chart.set_labels('X Label', 'Y Label')
chart.set_colors(['blue'])
chart.create_simple_bar()
```

### Available Charts:

- Bar chart with different variations
- Scatter plot with varying dot sizes
- Circle packing chart
- Hierarchical network chart
- Heatmaps
glauber-video-ferramentas 1.0
Este pacote irá fornecer ferramentas de processamento de vídeo
Keywords: camera,video,processamento

# Glauber Video Ferramentas

DescriÃ§Ã£o de como seu pacote funciona...

* Passos para instalaÃ§Ã£o
* Outras info. relevantes(autores, empresa, contato)
vkauth 1.0.0
Library that helps you to authenticate with VK.com using cookies from your browser. The library automatically fetches required cookies from supported browsers, establishes a session, and retrieves an access token
Requires Python: >=3.6

# VKAuth Library

VKAuth is a Python library that helps you to authenticate with VK.com using cookies from your browser. The library automatically fetches required cookies from supported browsers, establishes a session, and retrieves an access token.

## Features

- Automatic fetching of cookies from supported browsers
- Establishing a session with VK.com
- Fetching access token for user and group scopes

## Installation

Use the package manager [pip ↗](https://pip.pypa.io/en/stable/) to install VKAuth.

```bash
pip install vkauth
```

## Usage

```python
from vkauth import VKAuth

auth = VKAuth()

# to get a user token
user_token = auth.get_token()

# to get a group token
group_token = auth.get_token(group_id='your_group_id')
```

## Supported Browsers

- Google Chrome
- Yandex Browser
- Mozilla Firefox
- Microsoft Edge
- Opera
- Opera GX

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT ↗](https://choosealicense.com/licenses/mit/)

## Disclaimer
This library is intended to be used for educational purposes only. Never use it to exploit or harm someone's privacy without their consent. The maintainers of this library are not responsible for any misuse by the users.

## Contact
For any issues or suggestions, please create an issue on the GitHub repository.
botdata-sdk 0.0.3
Botdata SDK
Requires Python: >=3.8
Keywords: bodata-sdk

<!-- GETTING STARTED -->
## Getting Started

### Clone Repo
* Clone library repo
  ```sh
  git clone https://github.com/bot-auto/botdata-sdk.git
  cd botdata-sdk
  ```

### Venv
* Create virtual environment
  ```sh
  python3 -m venv venv
  source venv/bin/activate
  ```

### Install
* Run setup
  ```sh
  pip3 install .
  ```

### Pre-commit
* Get pre-commit register
  ```sh
  pre-commit install # install .pre-commit-config.yaml, automatic git commit check
  pre-commit run --all-files
  ```

### Release
* Update version
  Update version in pyproject.toml `version = "<new version>"`
* Run setup
  ```sh
  python3 setup.py sdist bdist_wheel # build dist and wheel
  twine upload dist/*   # upload to pypi
  ```
awspstore 1.4.3
Vault for your software project using AWS Parameter Store
Requires Python: >=3.7

# AWS Parameter Store
Parameters store (vault) for your backend software project using AWS Parameter Store

### Why AWS Parameter Store
Usually in every backend project there is a use of environment variables for holding values that can differ for each environment or need to be change from time to time.
Sometimes these parameters can hold sensitive data like passwords or other secrets, there for need to be stored in a safe place.

There are some solutions for that, some of them requires setup time, knowledge and additional servers (e.g. Hashicorp Vault).

One alternative for storing such data is AWS Parameter Store, this service ia part of the AWS Systems Manager service (https://docs.aws.amazon.com/systems-manager/latest/userguide/what-is-systems-manager.html)
AWS Parameter Store is a simple solution to store these parameters and tracking who change what and when.

### Example

In AWS Parameter Store every parameter has a path. 
Here for example we have two parameters for `dev` environment and two for stage.

    /dev/DB_HOST
    /dev/DB_USER
    /dev/DB_PWD
    /stage/DB_HOST
    /stage/DB_USER
    /stage/DB_PWD

To load parameters for `dev` environment see this code snippet

    from awspstore import get_parameters
    
    get_parameters(
        path='dev',  # Parameters path
        update_environ=True,  # Update the environ
        dump_parameters=True  # Dump all loaded parameters to standard output
    )

Parameters dump into standard output. Notice tha the password is masked, by default every parameter that it's name suggesting that it contains a password or other secret will be masked.  

    DEBUG	__init__.py(54) method: dump 	 DB_HOST: my.awesome-db.net
    DEBUG	__init__.py(54) method: dump 	 DB_USER: db_dev_user
    DEBUG	__init__.py(54) method: dump 	 DB_PWD: ***********

### Things to set before using 
Define these environment variables:

    AWS_DEFAULT_REGION = us-west-2
    AWS_ACCESS_KEY_ID = AKIAIOSFODNN7EXAMPLE
    AWS_SECRET_ACCESS_KEY = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
kodali 0.3.0
Korean Dataset Loading Inferface
Requires Python: >=3.8,<4.0

# KODALI(Korean Dataset Loading Interface)

# 1. 지원 데이터 셋
## 개체명 인식
- 모두의 말뭉치(Korean Corpus)
- KLUE-NER(KLUE)

## 관계 추출
- AI Hub: 한국어 지식기반 관계 데이터
- KLUE-RE

# 2. How to use
## 개체명 인식
```
dataset = Kodali(path={PATH}, task="ner", source={DATASET_SOURCE})

# Source List
# - 모두의 말뭉치 : "korean-corpus"
# - KLUE-NER : "klue"
```
# 3. 데이터 포맷
## 개체명 인식
개체명 인식 데이터 셋은 NerOutputs, NerData, NerEntity 포맷을 지원합니다.<br/>
NerEntity는 엔티티 정보를 가지고 있고, NerData는 문장과 문장 내 엔티티들을 포함합니다. NerOutputs는 NerData 목록을 가지고 있으며, 데이터를 입력할 시, 데이터의 크기를 자동으로 계산합니다.

```
NerOutputs
    data: List[NerData]
    size: int = 0

NerData:
    sentence: str
    entities: List[NerEntity]

NerEntity:
    word: str
    label: str
    start_idx: int
    end_idx: int
```