semvec/pypi-packages huggingface dataset:
textbuddy 0.3.4
Simple string and text utilities for Indian contexts for beginners.
Requires Python: >=3.7
Keywords: india, text, string, currency, phone, date, beginner, utilities

# textbuddy

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Simple and easy-to-use Python utilities for common string and text manipulation tasks in an Indian context. Designed with beginners in mind!

## Features

* **`format_rupee(amount)`**: Formats numbers into Indian Rupees (e.g., `₹ 12,34,567.89`).
* **`standardize_indian_phone(number_string)`**: Standardizes Indian phone numbers (e.g., `+919876543210`).
* **`parse_indian_date(date_string)`**: Parses common Indian date formats (e.g., `DD-MM-YYYY`).
* **`clean_text_basic(text)`**: Basic text cleaning (removes extra spaces, strips whitespace).
* **`remove_emoji(text)`**: Strips emojis from strings.

## Installation

You can install `textbuddy` using pip. First, make sure you have Python installed (version 3.7 or higher).

Then, open your Command Prompt (Windows) or Terminal (Mac/Linux) and type:

```bash
pip install textbuddy
paquete1 0.1.0
Una biblioteca para consultar cursos de hack4u

# Micro biblioteca de los cursos de S4vitar

Una micro biblioteca para consultar algunos de los cursos de s4vitar.

## Cursos disponibles:

-Introducción a Linux [15 horas]
-Personalización de Linux [3 horas]
-Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip install paquete1
```

## Uso básico

### Listar todos los cursos

```python
from paquete1 import list_courses

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

### Obtener un curso por nombre

```python
from paquete1 import course_by_name

course = course_by_name("Introduccion a Linux")
print(course)
```
### Calcular duración total de los cursos

```python3
from paquete1.utils import total_duration

print(f"Duración total: {total_duration()} horas)
```
aplicacion-de-ventas 0.1.3
Paquete para gestionar ventas, precios, impuestos y descuentos
Requires Python: >3.6

#Aplicación de Ventas

Este paquete proporciona funcionalidades para gestionar ventas, incluyendo
cálculos de precios, impuestos y descuentos.

##Instalación

Puedes instalar el paquete usando:

```bash
pip install .
"
pyanyzip 0.0.24
pyanyzip is a module to help with dealing with compressed files transparently
Requires Python: >=3.12
Keywords: bz2, gzip, python, zip

# *pyanyzip* project by Mark Veltzer

description: pyanyzip is a module to help with dealing with compressed files transparently

project website: https://veltzer.github.io/pyanyzip

author: Mark Veltzer

version: 0.0.24

![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

## github

![License](https://img.shields.io/github/license/veltzer/pyanyzip)

## build

![build](https://github.com/veltzer/pyanyzip/workflows/build/badge.svg)

## pypi

![PyPI - Status](https://img.shields.io/pypi/status/pyanyzip)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyanyzip)
![PyPI - License](https://img.shields.io/pypi/l/pyanyzip)
![PyPI - Package Name](https://img.shields.io/pypi/v/pyanyzip)
![PyPI - Format](https://img.shields.io/pypi/format/pyanyzip)

## pypi download

![PyPI - Downloads](https://img.shields.io/pypi/dd/pyanyzip)
![PyPI - Downloads](https://img.shields.io/pypi/dw/pyanyzip)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pyanyzip)



## contact me
[mailto](mailto:mark.veltzer@gmail.com)
![gitter](https://img.shields.io/gitter/room/veltzer/mark.veltzer)
![discord](https://img.shields.io/discord/719336281624281119)
![discord](https://img.shields.io/discord/719336282194444302)

Mark Veltzer, Copyright © 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
MLPredictiveModels 0.5
Educational regression models built from scratch in Python
Requires Python: >=3.6

# LinearRegression From Scratch

A minimal, easy-to-understand implementation of **Linear Regression** in Python using only NumPy.  
Designed for educational purposes to show how gradient descent optimizes linear models step by step.

## 🚀 Features

- Fits simple linear models with gradient descent
- Clean OOP class structure
- Pure NumPy implementation (no scikit-learn)
- Supports multi-feature data
- Easy to extend for regularization
- Printed training loss progress
- Simple `fit`, `predict`, and `evaluate` interface

---

## 📦 How to wrok?
from predictive_models import LinearRegression

# toy dataset
X_train = np.array([[1], [2], [3], [4], [5]])
y_train = np.array([2, 4, 6, 8, 10])

# initialize model
model = LinearRegression(learning_rate=0.01, epochs=1000)

# train
model.fit(X_train, y_train)

# predict
predictions = model.predict(X_train)

# evaluate
mse, r2 = model.evaluate(X_train, y_train)

print("Predictions:", predictions)
print(f"MSE: {mse:.4f}, R2: {r2:.4f}")
plsma 0.1.1
A developer's toolkit for managing and automating development tasks.
Requires Python: >=3.10

# Plasma
A developer's toolkit for managing and automating common development tasks.

## Overview
Plasma is a command-line utility that provides a collection of useful commands for developers, organized into categories for easy access.

## Installation
Install the package using `uv`:
```bash
uv add plsma
```

Or clone and install locally:
```bash
git clone https://github.com/zanderlewis/plasma.git
cd plsma
uv install -e .
```

## Usage
The main command is `plasma`. You can:
- List all available commands: `plasma` or `plasma list`
- List commands by category: `plasma list:<category>`
- Run a specific command: `plasma <command> [args]`
- Show version: `plasma --version`
- Show help: `plasma --help`

## Requirements
- Python 3.10+
- Dependencies: click, psutil, rich

## Development
This project uses:
- `uv` for dependency management
- `ruff` for linting and formatting

## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.