semvec/pypi-packages huggingface dataset:
practica-1-hack4u 0.1.1
Una práctica de biblioteca donde consultar cursos de hack4u.

# Hack4u Academy Courses Library

Una biblioteca en Python para consultar cursos de Hack4u.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de entornos [3 horas]
- Introducción al hacking [53 horas]
- Python Ofensivo [35 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso básico

## Listar todos los cursos

```python
from hacku import listar_cursos

for curso in listar_cursos():
    print(curso)
```

### Obtener un curso por nombre

```python
from hacku import buscar_curso_por_nombre

curso = buscar_curso_port_nombre("Introducción a Linux")
print(curso)
```

### Calcular duración total de los cursos

```python3
from hack4u.utils import duracion_total

print(f"Duración total: {duracion_total()} horas")
```
spro 0.1.0b0
Spro is a secure gateway to privacy-compliant AI interactions.
Requires Python: <4.0,>=3.12


# Spro Python API Library

Spro is a secure gateway to privacy-compliant AI interactions. It enables users to interact with language models without disclosing sensitive information. Spro automatically detects and masks sensitive data, ensuring that privacy and compliance standards are upheld. 

The Spro Python library provides convenient access to the SPRO REST API from any Python 3.8+ application. The library includes type definitions for all request parameters and response fields, offering both synchronous and asynchronous clients powered by httpx.

> **Note:** This library is currently in beta and under active development. Please report any issues or bugs you encounter.

## Installation

To install Spro, run the following command:

```bash
pip install spro
```

## Usage

To use Spro, import the `Spro` class from the `spro` module and create an instance of the class. You can then call the `secure` method to mask sensitive data in a given text.

```python
from spro import Spro

# Step 1: Set up the Spro client
client = Spro(api_key="your_api_key_here")

# Step 2: Mask sensitive information using the 'secure' method
result = client.secure("My name is John Doe, and my email is john.doe@example.com")

# Step 3: Print the masked result
print(result)
```

The `secure` method provides the following parameters:

| Field       | Type    | Description                                                       |
|-------------|---------|-------------------------------------------------------------------|
| `prompt`    | string  | The text you want to redact. This can be any string containing sensitive information. |
| `mask_type` | string  | The type of masking to apply. Options include: `char` (character masking), `label`(entity masking), `enhanced` (enhanced entity masking). |
| `mask_char` | string  | The character to replace sensitive data with (only used if `mask_type` is `char`). |
| `entities`  | array   | An optional array of specific entities to redact. This is an empty array by default. |
pr-ctica-1-hack4u 0.1.0
Una práctica de biblioteca donde consultar cursos de hack4u.

# Hack4u Academy Courses Library

Una biblioteca en Python para consultar cursos de Hack4u.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de entornos [3 horas]
- Introducción al hacking [53 horas]
- Python Ofensivo [35 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso básico

## Listar todos los cursos

```python
from hacku import listar_cursos

for curso in listar_cursos():
    print(curso)
```

### Obtener un curso por nombre

```python
from hacku import buscar_curso_por_nombre

curso = buscar_curso_port_nombre("Introducción a Linux")
print(curso)
```

### Calcular duración total de los cursos

```python3
from hack4u.utils import duracion_total

print(f"Duración total: {duracion_total()} horas")
```
dataquality-rules 0.1.0
This library is used for Data Quality
Requires Python: >=3.6

# Data Quality

This project provides a Data Quality Rule (DQR) enabler class for validating and reporting data quality metrics using Apache Spark and Jinja2. It allows users to perform various checks on a DataFrame, such as checking for null values, duplicates, uniqueness, range constraints, and values within a specific list. The results can be saved as an HTML report for easy review and sharing.

# Features

* Schema Validation : Compare the DataFrame's schema with an expected schema.
* Null Value Check : Identify the percentage of null values in specified columns.
* Duplicate Check : Find duplicate rows based on one or more columns.
* Uniqueness Check : Measure the uniqueness of values in specified columns.
* Range Check : Ensure column values fall within a defined range.
* Value Set Check : Verify if column values exist within a predefined list.
* HTML Report Generation : Automatically generate an HTML report summarizing all checks with visual tables.

# Installation

You can install the library using pip:

```bash
pip install dataquality_rules
```