semvec/pypi-packages huggingface dataset:
AmazonTicoTool 0.3
Tico package to extrack the following tables Buyablility_Status, General_information, Hazmat, Recall

# AmazonTicoTool

```bash
pip install AmazonTicoTool
```
## What is it?

**AmazonTicoTool** is a Python package that can fetch the data from Tico V3 tool and store them in a dataframe.

The function **get_data** has five returns attributes.

1. All attributes - This will return all the columns/attributes, which is roughly around 90 columns.
2. Buyability checks - This will return buyability related attributes only
3. Item General Information - This will return general information of the product.
4. Hazmat - This will return Hazmat related attributes.
5. Item Recall Restricted Diagnostics - This will return recalled item attributes.

## Main Feature and Usage
Here is the sample code to use the main function:

```python
# Importing the main function to fetch the data
from AmazonTicoTool import get_data

# Sample inputs to run the automation / the same can be replaced with original data
asins = ['B07KRJWN17', 'B002OY0QDQ', 'B000GCQ8X0']
sellers = [608808520, 608808520, 608808520]
marketplace = [1, 1, 1]

for asin, seller, mp in zip(asins, sellers, marketplace):
    dfs = get_data(asin, seller, mp)

# assigning five variables to extract the five dataframes
full, buyability, general, hazmat, recall = dfs
```

Each variable will have separate Dataframe, we can print/use them as desired.

## Where to get it
The source code is currently hosted on GitHub at:
https://gitlab.aws.dev/myteam/ramaviji/-/tree/main/Tico%20Package

Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/AmazonTicoTool) 


## Dependencies
The following packages would be required to use the package.
**pandas, selenium, webdriver_manager, time and browser_cookie3**
infraai 0.2.3
A minimalistic AI assistant designed to assist infrastructure engineers and developers with various tasks related to infrastructure management, deployment, and development.
Requires Python: >=3.9

# InfraAI
 A minimalistic AI assistant designed to assist infrastructure engineers and developers with various tasks related to infrastructure management, deployment, and development. 

🔭 InfraAI is a command-line tool designed to provide immediate concise responses. It leverages the capabilities of Google's Gemini models to deliver precise commands tailored to various operating systems and environments.


## Features 🌟

- **Streamlined Command Assistance**: Get specific commands for your operating system without unnecessary clutter.
- **Support for Multiple Gemeini Models**: Choose between different Gemini models
- **Easy Configuration**: Simple commands to set your API key and preferred Gemini model.


## Installation 🛠️

To install InfraAI, simply use pip. Ensure you have Python 3.9 or later installed.

```bash
pip install infraai
```

## Configuration 🔧

Before you start using InfraAI, you need to configure your Google Gemini API key. 

### Setting the API Key

```bash
infra --setkey
```

When prompted, enter your Google Gemini API key. This key will be saved and used for future requests.

### Setting the Gemini Model

If you want to use a different model than the default Gemini-pro, you can set it as follows:

```bash
infra --set-model gemini-1.5-flash
```

This command will save your model choice and use it for all future inquiries.

## Usage 📝

To use InfraAI, simply type `infra` followed by the command or query you need help with. Here’s how it works:

```bash
infra infra how to list docker containers
```
<img src="screencapture/dockerresponse.png" alt="response"/>

### Examples

Here are a few examples of how you can use InfraAI to get help with common infrastructure tasks:

- **Setting up nginx**:
```bash
infra setup nginx webserver
```

- **Initializing a Git repository**:
```bash
infra how to initialize git repository
```

- **Creating a Python virtual environment**:
```bash
infra create python virtual environment
```
dselib 0.1.3
NETVIBES / Data Science Experience
Requires Python: >=3.8

# NETVIBES / Data Science Experience 


## INTRODUCTION  

'Data Science Experience (DSE)' 는 NETVIBES 브랜드의 솔루션 포르폴리오 중 하나인 Solution 이름입니다.  
본 문서에서는 DSE 사용을 지원할 파이썬 패키지에 대한 설명을 다룹니다.  



## dselib 패키지 

DSE Library 패키지  

### 설치 

        pip install dselib

### 1. Excel File Transformer 

사용법  

fomr
furiosa-sdk 0.10.2
Furiosa SDK
Requires Python: ~=3.8

# Furiosa SDK

Furiosa™ SDK is a software development kit (SDK) for running deep-neural network inference using FuriosaAI NPU chips. It is a collection of libraries and command line tools including compiler and profiler. It also provides Python bindings that allow users to develop their application easily with abundant Python ecosystem, such as NumPy, Jupyter notebooks and scientific Python packages.

## Documentation
* [Furiosa SDK Document (English)](https://furiosa-ai.github.io/docs/latest/en)
* [Furiosa SDK Document (Korean)](https://furiosa-ai.github.io/docs/latest/ko)

## Installation

You can install Furiosa SDK with pip.

```sh
pip install furiosa-sdk
```

You can install also furiosa-sdk with extra packages (e.g., litmus, quantizer):

```sh
pip install 'furiosa-sdk[litmus,quantizer]'
```

The following are the extra packages:
* litmus: Command line tool to check if a model is compatible with furiosa-sdk
* models: Library which provides pretained models for Furiosa NPU
* quantizer: Library which quantizes and optimizes DNN models
* server: Serving framework enabling a DNN model to provide HTTP/GRPC endpoints
* serving: FastAPI-based Serving Library

## Releases
* [Furiosa SDK 0.10.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.10.0) (Latest)
* [Furiosa SDK 0.9.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.9.0)
* [Furiosa SDK 0.8.2](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/0.8.2)
* [Furiosa SDK 0.7.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.7.0)
* [Furiosa SDK 0.6.3](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.6.3)
* [Furiosa SDK 0.6.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.6.0)
* [Furiosa SDK 0.5.2](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.5.2)
* [Furiosa SDK 0.5.1](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.5.1)
* [Furiosa SDK 0.5.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.5.0)
* [Furiosa SDK 0.4.0](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.4.0)
* [Furiosa SDK 0.2.1](https://github.com/furiosa-ai/furiosa-sdk/releases/tag/v0.2.1)

## License

```
Copyright 2023 FuriosaAI, Inc.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
```
furiosa-runtime 0.10.2
Provide high-level Python APIs to access Furiosa AI's NPUs and its eco-system
Requires Python: ~=3.8

# Furiosa Runtime

This is a part of [Furiosa SDK] and provides high-level Python APIs to access
FuriosaAI's NPUs and ecosystem.

[Furiosa SDK]: https://pypi.org/project/furiosa-sdk/
furiosa-litmus 0.10.2
Furiosa Litmus, which readily checks whether a given model can be compiled with Furiosa SDK
Requires Python: ~=3.8

# Furiosa Litmus

This is a part of [Furiosa SDK] and provides a tool that readily checks whether
a given model can be quantized and compiled with Furiosa SDK.

[Furiosa SDK]: https://pypi.org/project/furiosa-sdk/
n0ryb 0.5.1
Una prueba para consultar biblioteca de la academia hack4u

# Hack4u Academy Prueba
Una biblioteca de prueba python para consultar cursos en hack4u.

## Curso Disponibles:

- Introduccion a linux [15 horas]
- Introduccion al hacking [53 horas]
- Personalizacion de linux [3 horas]

## Instalacion 

Instala el paquete usando `pip3`:
```python3
pip3 install n0ryb
```

## Uso basico

### Listar todos los cursos

```python

from n0ryb import list_courses
for course in list_courses():
    print(course)
```

# Obtener un curso por nombre

```python
from n0ryb import search_course
course =  search_course("Introduccion a linux")
print(course)
```

### Calcular duracion total de los cursos 
```python3

from n0ryb.utils import total_duration


print(f"Duracion Total: {total_duration()} horas")