semvec/pypi-packages huggingface dataset:
MayakApiClient-test-1 0.0.5
This is the simplest module for quick work with files.
Requires Python: >=3.9
Keywords: mayak_api_test

# mayak_api

python_version = 3.12

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pagecraft 0.0.6
Powerful package and easy to use that simplifies the creation of pagination in your apps
Requires Python: >=3.7

# pagecraft

Powerful package and easy to use that simplifies the creation of pagination in your apps

[![Downloads](https://static.pepy.tech/badge/pagecraft)](https://pepy.tech/project/pagecraft)
[![PyPI version](https://badge.fury.io/py/pagecraft.svg)](https://badge.fury.io/py/pagecraft)
![Repo Size](https://img.shields.io/github/repo-size/raulodev/pagecraft)
![PyPI - License](https://img.shields.io/pypi/l/pagecraft)

## Installing

```console
pip install pagecraft
```

## Tutorial

```python
from pagecraft import PageCraft

lista_de_objetos = [
    "Automatización Eficiente con Python",
    "Explorando las Profundidades de Python",
    "Desarrollo Web Moderno con Python y Flask",
    "Introducción a la Ciencia de Datos con Python",
    "Creando Aplicaciones de Escritorio con Python y PyQt",
    "Aventuras en el Aprendizaje Automático con Python",
    "Python y la Internet de las Cosas (IoT)",
]


pgcraft = PageCraft(lista_de_objetos)

# Get first page
page = pgcraft.page(1)
```

## PageCraftAlchemy Tutorial

```python
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker, declarative_base

from pagecraft.alchemy import PageCraftAlchemy

Base = declarative_base()

class Frases(Base):
    __tablename__ = "frases"
    id = Column(Integer, primary_key=True)
    texto = Column(String)

    def __str__(self):
        return self.texto

engine = create_engine("sqlite:///:memory:")

Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

for i in range(1, 21):
    frase = Frases(texto=f"Frase {i}")
    session.add(frase)

session.commit()

query = session.query(Frases)

alchemycraft = PageCraftAlchemy(query)

# Get first page
page = alchemycraft.page(1)
```



## Accessing data:

```python
# Get data from page
page.data

# True or False if next page exists
page.has_next_page

# Get the next page number
page.next_page

# True or False if prev page exists
page.has_prev_page

# Get the prev page number
page.prev_page

# Get the current page number
page.number
```

## Extend the data returned

```python
# set integer number as second argument

pgcraft = PageCraft(lista_de_objetos,20)

alchemycraft = PageCraftAlchemy(query , 20)
```

## Release Notes (0.0.6)

- add PageCraftAlchemy : create easy pagination for [Sqlalchemy ORM](https://docs.sqlalchemy.org/en/20/orm/quickstart.html)
- now there are 10 items returned by default
pro-video-ferramentas-2024 1.0
Este pacote irá fornecer ferramentas de processamento de vídeo
Keywords: camera,video,processamento

# Pro Video Ferramentas

DescriÃ§Ã£o de como funciona...

* Passos para instalaÃ§Ã£o
* Outras info. relevantes (autores, empresa, contato)
amazon-review-analyzer 1.1.1
Amazon Product Reviews Analyzer
Keywords: python,amazon,review,analyzer,sentiment,analysis,amazon review analyzer


# Amazon Review Analyzer

Welcome to the Amazon Review Analyzer, a Python program designed to help you make informed decisions about purchasing products on Amazon.

## Overview

This program takes an Amazon product link as input and analyzes the reviews, providing insights into whether the product is a good buy. However, the ultimate decision to purchase rests entirely with you.

## How It Works

1. **Input the Product Link:** When you provide the Amazon product link, the program initiates a web scraping process to extract reviews.

2. **Scraping and Data Storage:** The scrapper extracts reviews from the [amazon.in/product-reviews/asin](amazon.in/product-reviews/asin) page. The unique ASIN (Amazon Standard Identification Number) is extracted from the link using Python's regular expressions. Due to pagination, the program iterates through the pages, storing the reviews in a CSV file named `reviews.csv`.

3. **User Agent:** Replace **headers** variable  in  `scrapper.py` with your user agent to know what is your user agent simply google **my user agent**

4. **Sentiment Analysis:** The program uses the `vaderSentiment` Python package to analyze the tone of the reviews. Additionally, the `demoji` package is employed to handle emojis present in the reviews.

## Run the Amazon Review Analyzer

To use the Amazon Review Analyzer, follow these steps:

1. Install the `amazon_review_analyzer` package:

   ```bash
   pip install amazon_review_analyzer

2. ```bash
   from amazon_review_analyzer import get_sentiment

# The following code prompts the user to enter an Amazon URL in the terminal
# and displays the sentiment analysis results.

3. ```bash
   get_sentiment()