semvec/pypi-packages huggingface dataset:
drf-response-wrappers 1.2.7
DRF middleware to wrap all API responses
Requires Python: >=3.8

# DRF Response Wrapper

A Django REST Framework middleware that automatically wraps **all API responses** into a consistent format.  
No need to modify your views or APIView classes.

---

## 🚀 Features

- Standardized success & error responses
- Works with all DRF APIViews
- Simple integration: just add middleware
- No changes needed in your views

---

## 📦 Installation

Install the package from PyPI:

```bash
pip install drf-response-wrapper
```

---

## ⚙️ Usage / User Guide

### 1️⃣ Add Middleware

Open your Django project's `settings.py` and add the middleware:

```python
MIDDLEWARE = [
    # Other middlewares
    "drf_response_wrapper.middleware.APIResponseWrapperMiddleware",
]
```

> This middleware automatically wraps all DRF API responses, so you don't need to manually call `success_response()` or `error_response()` in your views.

---

### 2️⃣ Example API Responses

**Success response example:**

```json
{
  "success": true,
  "message": "Request successful",
  "status": 200,
  "data": {
    "id": 1,
    "name": "Example Item"
  }
}
```

**Error response example:**

```json
{
  "success": false,
  "message": "Something went wrong",
  "status": 400,
  "data": {}
}
```

---

### 3️⃣ Notes / Tips

- Works with any DRF APIView returning `Response`.
- Can be used with `BaseAPIView` or standard `APIView`.
- Ensures all API responses follow the same structure automatically.
- No changes needed in your existing views.
- Compatible with existing DRF projects—just add middleware.
- Ideal for projects where consistent API response format is required.

---

## 📝 License

MIT License
pyzstd 0.19.1
Support for Zstandard (zstd) compression
Requires Python: >=3.10
Keywords: compress, decompress, file, seek, seekable, tar, zst, zstandard, zstd

<div align="center" size="15px">

# pyzstd

Python bindings to Zstandard (zstd) compression library

[![GitHub build status](https://img.shields.io/github/actions/workflow/status/rogdham/pyzstd/build.yml?branch=master)](https://github.com/rogdham/pyzstd/actions?query=branch:master)
[![Release on PyPI](https://img.shields.io/pypi/v/pyzstd)](https://pypi.org/project/pyzstd/)
[![BSD-3-Clause License](https://img.shields.io/pypi/l/pyzstd)](https://github.com/Rogdham/pyzstd/blob/master/LICENSE.txt)

---

[📖 Documentation][doc]&nbsp;&nbsp;&nbsp;|&nbsp;&nbsp;&nbsp;[📃 Changelog](./CHANGELOG.md)

</div>

---

The `pyzstd` module provides Python support for [Zstandard](http://www.zstd.net), using
an API style similar to the `bz2`, `lzma`, and `zlib` modules.

> [!WARNING]
>
> Zstandard is now natively supported in Python’s standard library via the
> [`compression.zstd` module][compression.zstd]. For older Python versions, use the
> [`backports.zstd` library][backports.zstd] as a fallback.
>
> We recommend new projects to use the standard library, and existing ones to consider
> migrating.
>
> `pyzstd` internally uses `compression.zstd` since version 0.19.0.
>
> See [`pyzstd`'s documentation][doc] for details and a migration guide.

[doc]: https://pyzstd.readthedocs.io/
[compression.zstd]: https://docs.python.org/3.14/library/compression.zstd.html
[backports.zstd]: https://github.com/Rogdham/backports.zstd
polargini 1.0.0
Python implementation of the Polar Gini Curve
Requires Python: >=3.9
Keywords: gini, spatial, single-cell, rna-seq

# Python Polar Gini Curve

[![PyPI version](https://badge.fury.io/py/polargini.svg)](https://pypi.org/project/polargini/)
[![CI](https://github.com/cytronicoder/polar-gini-curve/actions/workflows/ci.yml/badge.svg)](https://github.com/cytronicoder/polar-gini-curve/actions/workflows/ci.yml)

This is a Python port of the Polar Gini Curve introduced by Nguyen et al. (2021). I've implemented the core functionality in Python, making it easier to integrate into Python-based data analysis workflows. The original MATLAB code is available in the [aimed-lab/Polar-Gini-Curve repository](https://github.com/aimed-lab/Polar-Gini-Curve).

## Quickstart

To get started with the Polar Gini Curve in Python, you can install the package from PyPI, then use the CLI to compute the Polar Gini Curve from a CSV file containing your data. Here's a quick example:

```bash
pip install polargini
pgc --csv data.csv --plot
```

For more detailed usage, refer to the [original documentation](https://github.com/aimed-lab/Polar-Gini-Curve/blob/main/README.md).

## Citation

Nguyen, T.M., Jeevan, J.J., Xu, N. and Chen, J.Y., "Polar Gini Curve: a technique to discover gene expression spatial patterns from single-cell RNA-seq data," _Genomics, Proteomics & Bioinformatics_ 19(3), 493-503 (2021).