semvec/pypi-packages huggingface dataset:
geometry-to-spatialite 0.6.0
Import geographic and spatial data from files into a SpatiaLite DB
Requires Python: >=3.10

# geometry-to-spatialite

[![Run tests](https://github.com/chris48s/geometry-to-spatialite/actions/workflows/test.yml/badge.svg?branch=master)](https://github.com/chris48s/geometry-to-spatialite/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/chris48s/geometry-to-spatialite/branch/master/graph/badge.svg?token=Y15Y63PPM4)](https://codecov.io/gh/chris48s/geometry-to-spatialite)
[![PyPI Version](https://img.shields.io/pypi/v/geometry-to-spatialite.svg)](https://pypi.org/project/geometry-to-spatialite/)
![License](https://img.shields.io/pypi/l/geometry-to-spatialite.svg)
![Python Compatibility](https://img.shields.io/badge/dynamic/json?query=info.requires_python&label=python&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fgeometry-to-spatialite%2Fjson)
![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

Import geographic and spatial data from files into a SpatiaLite DB.

## 📚 [Documentation](https://chris48s.github.io/geometry-to-spatialite)
* [Installation](https://chris48s.github.io/geometry-to-spatialite/installation.html)
* [Usage](https://chris48s.github.io/geometry-to-spatialite/usage.html)
* [Troubleshooting](https://chris48s.github.io/geometry-to-spatialite/troubleshooting.html)
pandas-outliers 1.0.1
A simple library that adds .outliers() method to pandas DataFrame for easy outlier detection.
Requires Python: >=3.8

# 🧠 pandas-outliers

`pandas-outliers` is a simple and powerful Python library that extends the functionality of **Pandas DataFrames** by adding an easy one-line method to detect outliers in your data.

---

## 🚀 Features
✅ Detect outliers in any numeric column with **just one line**  
✅ Works with **IQR (Interquartile Range)**, **Z-score**, and **Isolation Forest** methods  
✅ 100% compatible with Pandas DataFrames  
✅ Lightweight, beginner-friendly, and fast  

---

## 💡 Installation
You can install it using pip:
```bash
pip install pandas-outliers

import pandas as pd
import pandas_outliers

# Sample data
data = pd.DataFrame({
    "age": [22, 23, 25, 45, 23, 24, 120],
    "salary": [25000, 27000, 26000, 28000, 1000000, 29000, 27500]
})

# Detect outliers in one line 👇
outliers = data.outliers()

print(outliers)

output
   age   salary
6   120  1000000


⚙️ Methods Supported

IQR (default) – detects values outside 1.5×IQR range

Z-score – detects data points beyond a given threshold

Isolation Forest – detects anomalies using machine learning

🧑‍💻 Author

Kiran Hamza
📍 Python & Data Science Enthusiast
💌 [Your Email or GitHub Profile Here]

🪪 License

This project is licensed under the MIT License — see the LICENSE
 file for details.

⭐ Tip: If you like this library, please star the repo or share it with your data science friends!


---

## 📜 2️⃣ LICENSE (MIT License — copy this into a file named `LICENSE`)
```text
MIT License

Copyright (c) 2025 Kiran Hamza

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights  
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell      
copies of the Software, and to permit persons to whom the Software is          
furnished to do so, subject to the following conditions:                       

The above copyright notice and this permission notice shall be included in     
all copies or substantial portions of the Software.                            

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR     
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,       
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE    
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER         
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,  
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE  
SOFTWARE.
plato-learn 1.4.3
Plato: a research framework for federated learning
Requires Python: >=3.13

# Plato: A Research Framework for Federated Learning

Welcome to *Plato*, a software framework to facilitate scalable, reproducible, and extensible federated learning research. Please refer to the documentation website, available at the [Plato documentation website](https://platodocs.netlify.app/), for more details on installing, running, configuring, extending, and deploying Plato.
wing-carrier 1.2.0
Send commands from Wing-IDE to DCC apps or between DCC apps
Requires Python: >=3.4

# wing-carrier
Used to send commands between dcc applications with build-in support for the wing-ide.

This will soon be intergrated in my wing-ide-maya project.  If you're looking to get up and running with Maya, please check:
https://github.com/Nathanieljla/wing-ide-maya

If you implement your own DCC Pigeon I'd love collab, to grow this repository.
github-backup-supabase 1.0.5
A CLI tool to back up your GitHub repositories and upload to Supabase storage.

# GitHub Backup CLI

A simple command-line tool to back up all your GitHub repositories locally or to Supabase storage so you will never lose your code again.

[![PyPI](https://img.shields.io/pypi/v/github-backup-supabase)](https://pypi.org/project/github-backup-supabase/)

## Installation

```bash
pip install github-backup-supabase
```

## Usage

```bash
# Basic backup (requires username)
github-backup-supabase run --username your-github-username

# Backup with token (for private repos)
github-backup-supabase run --username your-username --token ghp_xxxxx

# Backup and upload to Supabase
github-backup-supabase run --username your-username --upload --url https://xxx.supabase.co --key your-supabase-key --bucket your-bucket-name
```


## Environment Variables

You can set these instead of passing flags:

```bash
GITHUB_USERNAME=your-username
GITHUB_TOKEN=ghp_xxxxx
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=your-key
BUCKET_NAME=your-bucket-name
```

## Options

- `--username` - GitHub username **(required)**
- `--token` - GitHub token (optional, for private repos)
- `--output` - Custom folder name (default: timestamped)
- `--upload` - Upload to Supabase after backup
- `--url` - Supabase project URL
- `--key` - Supabase key (anon or service key)
- `--bucket` - Supabase bucket name (default: `github-backups`)

## What it does

1. Fetches all repositories from the GitHub user
2. Clones each repo to a local folder
3. Creates a ZIP archive
4. (Optional) Uploads to Supabase Storage
5. Cleans up local files

---

**Author:** Psycho Coder  
**GitHub:** [@itspsychocoder](https://github.com/itspsychocoder)  
**Website:** [hussnainahmad.tech](https://hussnainahmad.tech/)