semvec/pypi-packages huggingface dataset:
awkward_kaitai 0.11.2
Kaitai Struct runtime for Awkward Arrays
Requires Python: >=3.8

# Kaitai Struct: Awkward Array runtime

This project bridges Kaitai Struct schemas to Awkward Arrays. It provides a C++
runtime plus Python helpers so Kaitai-generated parsers can emit Awkward Array
buffers directly.

## Quickstart

1) Generate C++ from a `.ksy` schema using the Kaitai compiler with the Awkward target.
2) Build a shared library from the generated `.cpp`.
3) Load binary data into an Awkward Array.

Example flow:

```bash
# From a schema (requires kaitai-struct-compiler with awkward target)
java -cp <classpath> io.kaitai.struct.JavaMain -t awkward --outdir test_artifacts example_data/schemas/hello_world.ksy

# Build a shared library from the generated C++ file
awkward-kaitai-build test_artifacts/hello_world.cpp -b build
```

```python
from awkward_kaitai import Reader

reader = Reader("test_artifacts/libhello_world.so")
array = reader.load("example_data/data/hello_world.bin")
print(array)
```

## Documentation

The documentation is available at <https://det-lab.github.io/kaitai_struct_awkward_runtime/>.

## Requirements

- Kaitai Struct compiler **0.11+** with the `awkward` target.
- CMake and a C++ toolchain for building shared libraries.

## Development

- Build and test everything: `make test`
- Fast loop for a single schema: `make compile_test testcase=hello_world`
- Linting and checks: `nox -s lint` / `nox -s pylint` / `nox -s tests`
- Submodule setup: `git submodule update --init --recursive`

See `AGENTS.md` for detailed contributor guidance.

## Related Papers and Talks
1. [Describe Data to get Science-Data-Ready Tooling: Awkward as a Target for Kaitai Struct YAML](https://indico.cern.ch/event/1330797/contributions/5796564/), Advanced Computing and Analysis Techniques for Physics Research Workshop 2024, New York, US.
2. [Awkward Target for Kaitai Struct](https://indico.cern.ch/event/1252095/contributions/5592420/), PyHEP Users Workshop 2023.
Ctack14-practice-data-sets 0.1.21
Australian weather data used to learn and practice data analysis skills.
Requires Python: >=3.10

# Australian Weather Data Analysis

---
This project loads daily Australian weather data over 10 years
and stores it in a pandas DataFrame. It's likely that this 
project will expand to perform more advanced data analysis.

## Dataset
- Title: Australia Weather Data
- Source: Kaggle

## Usage
Run main.py to read and display weather data to the console.

## Technologies
- Python 3.10
- pandas

## Installation
Make sure you have:
- Python 3.10 or higher installed
- Build installed: `pip install build`

To install the required dependencies, run the following command:
```bash 
pip install Ctack14-practice-data-sets
```
---
To Install as a developer:
```bash
pip install Ctack14-practice-data-sets[dev]
```


## Steps

For module 3, I restructured the project to use OOP principles while still maintaining the ability to be packaged and uploaded to PyPI.

- `DataLoader` class to handle loading and preprocessing the weather data.
- `DataProcessor` class to perform analysis on the loaded data. Description now returns basic statistics rather than displaying them. They can be displayed in the application logic.
- `app.py` to serve as the main entry point for running the application and composes the objects.

 ---

The init file for the package was updated to reflect the new structure.

### OOP Principles Applied
- Encapsulated data and behavior inside of classes.
- Used methods to operate on the data within the classes.
- Separated concerns by creating distinct classes for loading and processing data.
- Created an application class to manage the workflow. This replaces the old `main.py` script.