semvec/pypi-packages huggingface dataset:
lapis-api 0.2.1.post1
An organized way to create REST APIs
License: MIT
Requires Python: >=3.9

Lapis is a file-based REST API framework inspired by Modern Serverless Cloud Services

To create a basic Lapis server, create a folder called *api* and create python script named *path.py*
then within your main folder create a python script to start the server (we will call this script *main.py* in our example)

Your project directory should look like this:

```
project-root/
|-- api/
|  |-- path.py
`-- main.py
```

Then within the *api/path.py* file create your first GET api endpoint by adding the following code:
```py
from lapis import Response, Request

async def GET (req : Request) -> Response:
    return Response(status_code=200, body="Hello World!")
```

Finally by adding the following code to *main.py* and running it:
```py
from lapis import Lapis

server = Lapis()

server.run("localhost", 80)
```

You can now send an HTTP GET request to localhost:80 and recieve the famous **Hello World!** response!
smartnoise-synth 1.0.6
Differentially Private Synthetic Data
License: MIT
Requires Python: <3.13,>=3.9

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Python](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue)](https://www.python.org/)

<a href="https://smartnoise.org"><img src="https://github.com/opendp/smartnoise-sdk/raw/main/images/SmartNoise/SVG/Logo%20Mark_grey.svg" align="left" height="65" vspace="8" hspace="18"></a>

# SmartNoise Synthesizers

Differentially private synthesizers for tabular data.  Package includes:
* MWEM
* MST
* QUAIL
* DP-CTGAN
* PATE-CTGAN
* PATE-GAN
* AIM

## Installation

```
pip install smartnoise-synth
```

## Using

Please see the [SmartNoise synthesizers documentation](https://docs.smartnoise.org/synth/index.html) for usage examples.

## Note on Inputs

MWEM and MST require columns to be categorical. If you have columns with continuous values, you should discretize them before fitting.  Take care to discretize in a way that does not reveal information about the distribution of the data.

## Communication

- You are encouraged to join us on [GitHub Discussions](https://github.com/opendp/opendp/discussions/categories/smartnoise)
- Please use [GitHub Issues](https://github.com/opendp/smartnoise-sdk/issues) for bug reports and feature requests.
- For other requests, including security issues, please contact us at [smartnoise@opendp.org](mailto:smartnoise@opendp.org).

## Releases and Contributing

Please let us know if you encounter a bug by [creating an issue](https://github.com/opendp/smartnoise-sdk/issues).

We appreciate all contributions. Please review the [contributors guide](../contributing.rst). We welcome pull requests with bug-fixes without prior discussion.

If you plan to contribute new features, utility functions or extensions to this system, please first open an issue and discuss the feature with us.
django-advanced-report-builder 1.2.16
Django app that allows you to build reports from modals
License: MIT
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/django-advanced-report-builder.svg)](https://badge.fury.io/py/django-advanced-report-builder)


# Django advanced report builder

A Django application that provides a fully featured, dynamic report-building interface. It allows users to create, customise, preview and export reports directly through a dedicated front-end UI, without writing queries or touching the Django admin.

## Features

- Build reports through a standalone report-builder interface (not the Django admin).
- Choose a root model and dynamically select fields to display.
- Add filters, conditions, ordering and grouping.
- Preview results instantly within the UI.
- Export to CSV, Excel and other supported formats.
- Pluggable architecture for adding formats, custom filters, or integration hooks.
- Designed to integrate easily into existing Django projects.