semvec/pypi-packages huggingface dataset:
directory-info-extractor 0.1.1
A lightweight Python library for extracting comprehensive information from directories
Requires Python: >=3.8

# Directory Info Extractor

A lightweight Python library for extracting comprehensive information from directories. It provides an easy way to retrieve project structure and file contents, with customizable inclusion and exclusion patterns. Ideal for project analysis and documentation.

## Installation

You can install the Directory Info Extractor using pip:

```
pip install directory-info-extractor
```

## Usage

Here's a quick example of how to use the Directory Info Extractor:

```python
from directory_info_extractor import get_directory_info

info = get_directory_info(
    "/path/to/your/directory",
    include_patterns=["*.py"],
    exclude_patterns=["*.pyc", "__pycache__"],
    recursive=True
)
print(info)
```

## Features

- Retrieve project structure
- Extract file contents
- Customizable file and directory inclusion patterns
- Customizable file and directory exclusion patterns
- Optional recursive directory traversal
- Optional logging of results

## Main Function Parameters

- `dir_path` (str): Path to the local directory.
- `include_project_structure` (bool): Include project structure in the output.
- `include_file_contents` (bool): Include file contents in the output.
- `include_patterns` (List[str], optional): Patterns to include (e.g., ["*.py", "specific_folder/*"]).
- `exclude_patterns` (List[str], optional): Patterns to exclude.
- `recursive` (bool): Whether to recursively search subdirectories.
- `log_path` (str, optional): Path to save the log.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
aspect-library 0.1.0
A Python library for aspect-based sentiment analysis with translation capabilities
Requires Python: >=3.6

# My Aspect Library

## Overview

My Aspect Library is a Python package designed for performing aspect-based sentiment analysis with integrated translation capabilities. This library allows you to easily translate text, extract aspects, and analyze sentiment, making it a powerful tool for natural language processing tasks.

## Features

- **Translation**: Automatically translate text in your dataset to the target language before analysis.
- **Aspect Extraction**: Extract aspect terms from text using state-of-the-art models.
- **Sentiment Analysis**: Analyze sentiment associated with extracted aspects.
- **Data Processing**: Clean and process text data for analysis, including stopword removal and text normalization.
- **Pivot Table Generation**: Create pivot tables to summarize sentiment analysis results.

## Installation

To install the package, you can simply clone the repository and use `setup.py` to install it:

```bash
git clone https://github.com/yourusername/my_aspect_library.git
cd my_aspect_library
pip install .
```

Alternatively, if you want to install it in editable mode:

```bash
pip install -e .
```

## Usage

Hereâ€™s a quick example of how to use the library:

```python
import pandas as pd
from my_aspect_library import AspectExtractor, translate_aspects, create_pivot_table, concatenate_results

# Load your dataset
df = pd.read_excel('path_to_your_file.xlsx')

# Initialize the aspect extractor
aspect_extractor = AspectExtractor()

# Perform translation and aspect extraction in one step
result_df = aspect_extractor.extract(df, column_name='Customer Comments', target_language='en')

# Translate aspects and sentiments
translated_aspects = translate_aspects(result_df)

# Create pivot table for sentiment analysis
pivot_table = create_pivot_table(translated_aspects)

# Save or further process your results as needed
```

## Dependencies

- `pandas`
- `deep_translator`
- `unlimited_machine_translator`
- `pyabsa`
- `nltk`

These dependencies are automatically installed when you install the package.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

If you want to contribute to this project, feel free to fork the repository and submit a pull request.

## Acknowledgments

Special thanks to all the contributors and maintainers of the libraries that this project depends on.
helper-for-maxi 1.2.4.post2
Contains all my Functions
Requires Python: >=3.12

# Helper for Maxi

All the Functions I could need multiple times

## Instructions

1. Install:

```sh
pip install helper-for-maxi
```

2. Import package:

```py
import maximyoga
import maximyoga.*
from maximyoga import *
from maximyoga.* import *
# You can replace * with what you need!
```
exeriop 0.1.0
Una biblioteca para consultar cursos de Hack4u.

# Hack4u Academy Courses Library

 Una Biblioteca Python para consutar los cursos de Hack4u.

## Cursos Disponibles

- Introduccion a linux [ 15 horas ]
- Personalizacion de Linux [ 3 horas ]
- Introduccion al Hacking [ 53 horas ]
- Python Ofensivo [ 35 horas ]

## Instalacion

Instala el paquete usando `pip3`:

```python3
pip3 install Hack4u
```

## Uso basico 

### Listar todos los cursos

```python
from Hack4u import list_courses

for course in courses():
    print(course)
```

### Obtener curso por nombre

```python
form Hack4u import get_course_by_name

course = get_course_by_name("Introduccion a Linux")
print(course)
```

### Calculadora duracion total de los cursos

```python3
from Hack4u.untils import total_duration

print(f"Duracion total: {total_duration()} horas")
```