semvec/pypi-packages huggingface dataset:
ash2048 1.0.0
A gym environment for playing 2048.
Requires Python: >=3.10,<4.0

# ash2048
---

> Implementation of the 2048 Game for Gym Environments with state rendering through PyGame.

## Usage and Notes:

The package follows gymnasium conventions.
To render the board while playing, set `display=True` in the constructor.
To save the render to a gif, set `save_animation=True` in the constructor.
To save the generated animation to a file, call `save_render(filename)` where the filaname is the outfile gif file.
The animation is reset upon calling `reset()`.
grading-tools 0.19.0
Tools for evaluating student submissions.
Requires Python: >=3.10,<3.13

# Grading Tools

[![build](https://github.com/worldquant-university/grading-tools/actions/workflows/build.yml/badge.svg)](https://github.com/worldquant-university/grading-tools/actions)
[![codecov](https://codecov.io/gh/worldquant-university/grading-tools/branch/main/graph/badge.svg?token=PV83R6T99N)](https://codecov.io/gh/worldquant-university/grading-tools)

This library allows you to compare student submissions to an answer, and provide
meaningful feedback. It currently accommodates basic Python data structures, `pandas`
Series and DataFrames, `scikit-learn` models, and images.

## Installation

```bash
$ pip install grading-tools
```

## Usage

```python
>>> from grading_tools.graders import PythonGrader
>>> sub = {"snake": "reptile", "frog": "reptile"}
>>> ans = {"snake": "reptile", "frog": "amphibian"}
>>> g = PythonGrader(sub, ans)
>>> g.grade_dict()
>>> g.return_feedback(html=False)
{
    'score': 0,
    'passed': False,
    'comment': "The value for the key `frog` doesn't match the expected result."
}
```

## License

`grading-tools` was created by
[Nicholas Cifuentes-Goodbody](https://github.com/ncgoodbody) at
[WorldQuant University](http://wqu.edu/). It is not currently licensed for reuse of
any kind.

## Contributing

This package uses [Python Semantic Release](https://python-semantic-release.readthedocs.io/en/latest/), so all commit messages must follow the [Angular](https://github.com/angular/angular/blob/main/CONTRIBUTING.md#-commit-message-format) format.
blitzmanager 0.0.7
Simple high level manager that uses package managers for C/C++ dependencies.
Requires Python: >=3.8.10

# BlitzManager 
BlitzManager is a simple adjustable manager that uses package managers for C/C++ dependencies. It can also
be used to create CMake project templates.



## Quick Start
Here is a simple example for installing dependencies using [vcpkg](https://github.com/microsoft/vcpkg).

```python
from blitzmanager import BlitzManager,Path, SupportedManagers
    
import os

cwd = os.path.dirname(os.path.realpath(__file__))
manager_output = Path(cwd, "out")
build_output = Path(cwd, "out", "dependencies")
install_path = Path(cwd, "out", "install")

flags = {
        "--output_dir": {
            "required": False,
            "default": None,
            "type": str,
            "help": "Chose another output directory."
        }
    }
manager = BlitzManager()

manager.add_flags(flags)
manager.parse_arguments()

if manager["output_dir"] is not None:
    manager_output = Path(manager["output_dir"])
    assert not manager_output.is_file()
    assert manager_output.is_abs(check_if_exists=False)
    build_output = Path(manager_output.path, "dependencies")
    install_path = Path(manager_output.path, "install")    
    
manager.initialize(manager_output, build_output, install_path, SupportedManagers.VCPKG)
manager.initialize_managers()
manager.build_via_package_manager(["zlib", "sqlite3"])

manager.build_dependencies()
        
```
## Disclaimer
This tool is still under development and should be used with caution.
datasci-agents 0.1.0
Datasci Agents
Requires Python: >=3.9,<4.0


<p align="center">
  <img src="media/image.jpg" height="200" alt="Datasci Agents fig"/>
</p>

Data science agents are LLM-powered helpers designed to help with routine tasks around EDA, feature engineering and modelling.

Although they are able to deliver full datasci projects on their own (and even compete on Kaggle fully autonomously), they are primarily designed as assistants relying on careful human steering.

# Installation
TODO

# Kaggle integration
Coming soon!
pyFinazon 0.1
finazon wrapper

# pyFinazon

A Python package for fetching financial data from the Finazon API.

## Installation

```bash
pip install finazon
```
[![Python package](https://github.com/bossdown123/pyFinazon/actions/workflows/python-package.yml/badge.svg?branch=main)](https://github.com/bossdown123/pyFinazon/actions/workflows/python-package.yml)