semvec/pypi-packages huggingface dataset:
python-pickaxe 0.5.5
HTML data extraction library
License: MIT
Requires Python: <4.0,>=3.10

# Pickaxe

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/python-pickaxe?style=flat-square)
![PyPI - Version](https://img.shields.io/pypi/v/python-pickaxe?style=flat-square)

Pickaxe is a Python package for structured data extraction from HTML documents. It provides a simple and intuitive API for parsing HTML documents, and automatically extracting structured data from them.

## Features

- **Written in Rust**: Pickaxe is written in Rust, which makes it fast and memory-efficient.
- **Robust**: Pickaxe uses the `html5ever` and `selectors` crate for browser-grade HTML parsing and CSS selector matching.
- **CSS Selectors & XPath**: Pickaxe supports both CSS selectors and (simple) XPath expressions for querying HTML documents.

## Quick Start

### Python

#### Installation

```bash
pip install python-pickaxe
```

#### Basic Usage

```python
from pickaxe import HtmlDocument

# Parse an HTML document
document = HtmlDocument.from_str("<html><body><h1>Hello, World!</h1></body></html>")

# Access elements using CSS selectors or XPath expressions
heading = document.find("h1")
print(heading.inner_text)  # Output: Hello, World!

heading = document.find_xpath("//h1")
print(heading.inner_text)  # Output: Hello, World!
```

### Rust

#### Installation

```bash
cargo add rust-pickaxe
```

#### Basic Usage

```rust
use pickaxe::HtmlDocument;

fn main() {
    // Parse an HTML document
    let document = HtmlDocument::from_str("<html><body><h1>Hello, World!</h1></body></html>").unwrap();

    // Access elements using CSS selectors or XPath expressions
    let heading = document.find("h1").unwrap();
    println!("{}", heading.inner_text());  // Output: Hello, World!

    let heading = document.find_xpath("//h1").unwrap();
    println!("{}", heading.inner_text());  // Output: Hello, World!
}
```


## License
This project is licensed under MIT License.

## Support & Feedback
If you encounter any issues or have feedback, please open an issue. We'd love to hear from you!

Made with ❤️ by Emergent Methods
tempo4d 0.1.8
A 4D-STEM and diffraction data processing pipeline using ML object detection
License: MIT
Requires Python: >=3.9
Keywords: 4D-STEM, diffraction, TEM, electron microscopy, object detection, YOLOv8, machine learning


# tempo4d

A PyTorch-based 4D-STEM analysis toolkit with real-time visualization, diffraction processing, and YOLOv8 integration.