semvec/pypi-packages huggingface dataset:
suthing 0.5.1
Small Useful Things : python package for convenient primitives
Requires Python: >=3.11
Keywords: suthing, tools

# SUThing <img src="docs/assets/favicon.ico" alt="suthing logo" style="height: 32px; width:32px;"/>

SUThing /ˈsu.θɪŋ/ or /ˈsʌ.θɪŋ/ (Some Useful Things) is a collection of useful classes and decorators.  

A Python utility package providing tools for file handling, timing, profiling, and data comparison.

![Python](https://img.shields.io/badge/python-%3E=3.11-blue?logo=python)
[![PyPI version](https://badge.fury.io/py/suthing.svg)](https://badge.fury.io/py/suthing)
[![PyPI Downloads](https://static.pepy.tech/badge/suthing)](https://pepy.tech/projects/suthing)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![pre-commit](https://github.com/growgraph/suthing/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/growgraph/suthing/actions/workflows/pre-commit.yml)

## Features

- **File Handling**: one-line file reading/wriing with file format infererence from provided extension (YAML, JSON, CSV, pickle, gz etc.)
- **Performance Measurement**: Simple timer utilities and profiling decorators
- **Data Comparison**: Deep comparison of nested data structures
- **Error Handling**: Decorators for secure function execution and error tracking

## Documentation
Full documentation is available at: [growgraph.github.io/suthing](https://growgraph.github.io/suthing)

## Installation

```bash
pip install suthing
```

## Usage Examples

### File Handling

```python
from suthing import FileHandle, FileType

# Read YAML file
data = FileHandle.load(fpath="config.yaml")

# Write compressed JSON
# file type inferred from extension
FileHandle.dump(data, "output.json.gz")
```

### Timing Code

```python
from suthing import Timer

with Timer() as t:
    # Your code here
    pass
print(f"Execution took {t.elapsed_str}")
```

### Profiling Functions

```python
from suthing import profile, SProfiler

profiler = SProfiler()

@profile(_argnames="input_size")
def my_function(input_size):
    # Function code
    pass

# Run with profiler
my_function(input_size=100, _profiler=profiler)

# View results
stats = profiler.view_stats()
```

### Deep Comparison

```python
from suthing import equals

# Compare nested structures
result = equals(complex_dict1, complex_dict2)
```

## Requirements

- Python 3.10+
- pandas
- PyYAML
- python-dotenv

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
ploston-runner 0.1.0
Local runner for Ploston - executes workflows on your machine
Requires Python: >=3.12

# Ploston Runner

Local runner for Ploston - executes workflows on your machine with access to local MCPs (filesystem, docker, git, shell).

## Installation

```bash
pip install ploston-runner
```

## Usage

```bash
# Connect to Control Plane
ploston-runner connect --token <your-token> --cp-url wss://cp.example.com/runner --name my-laptop

# Or use environment variables
export PLOSTON_RUNNER_TOKEN=your-token
export PLOSTON_CP_URL=wss://cp.example.com/runner
export PLOSTON_RUNNER_NAME=my-laptop
ploston-runner connect
```

## Configuration

Create a config file `~/.ploston/runner.yaml`:

```yaml
control_plane: "wss://cp.example.com/runner"
auth_token: "ploston_runner_xxxxx"
runner_name: "my-laptop"
```

## License

Apache-2.0
beautifyall 0.3.0
Библиотека для красивого вывода списков в консоль
Requires Python: >=3.6

# BeautifyAll рџЋЁ

Р‘РёР±Р»РёРѕС‚РµРєР° РґР»СЏ С‚РµС…, РєРѕРјСѓ РЅР°РґРѕРµР»Рѕ СЃРјРѕС‚СЂРµС‚СЊ РЅР° РЅРµРєСЂР°СЃРёРІС‹Рµ РІС‹РІРѕРґС‹ `[1, 2, 3]` РІ РєРѕРЅСЃРѕР»Рё. РџСЂРµРІСЂР°С‰Р°Р№С‚Рµ СЃРїРёСЃРєРё РІ Р°РєРєСѓСЂР°С‚РЅС‹Рµ РєРѕР»РѕРЅРєРё, РЅСѓРјРµСЂРѕРІР°РЅРЅС‹Рµ РїРµСЂРµС‡РЅРё РёР»Рё Р°Р»С„Р°РІРёС‚РЅС‹Рµ СЃРїРёСЃРєРё РѕРґРЅРѕР№ РєРѕРјР°РЅРґРѕР№.

## Р­РєСЃРїРѕСЂС‚ РґР°РЅРЅС‹С…

Р’С‹ РјРѕР¶РµС‚Рµ СЃРѕС…СЂР°РЅСЏС‚СЊ СЃРІРѕРё РїРµСЂРµРјРµРЅРЅС‹Рµ РІ СЂР°Р·РЅС‹Рµ С„РѕСЂРјР°С‚С‹:

```python
from beautifyall import save_in_data, get_vars

x = [1, 2, 3]
y = {"a": 1}

# РЎРѕС…СЂР°РЅРёС‚СЊ РІСЃС‘ РІ JSON
save_in_data(get_vars(globals()), "json")

# РЎРѕС…СЂР°РЅРёС‚СЊ С‚РѕР»СЊРєРѕ РІС‹Р±СЂР°РЅРЅС‹Рµ РІ SQL (Р±Р°Р·Р° РґР°РЅРЅС‹С… SQLite)
save_in_data(get_vars(globals(), names=["x"]), "sql", filename="my_database")
```

## РЈСЃС‚Р°РЅРѕРІРєР°
```bash
pip install beautifyall
```
bus2002 0.1.4
Curated datasets for WLU BUS 202 Business Analytics
Requires Python: >=3.7

# BUS 2002 Datasets

A Python package containing curated datasets for Washington and Lee University's BUS 202/215: Business Analytics course.

## Installation

```bash
pip install bus2002
```

## Usage

```python
from bus2002 import load_students

# Load student data
students_df = load_students()
print(students_df.head())

# List all available datasets
from bus2002 import list_datasets
list_datasets()
```

## Available Datasets

1. **students.csv** - Sample student data with names and ages

## Features

- 📦 Datasets bundled with the package (works offline after installation)
- 🚀 Easy-to-use loading functions
- 📚 Built-in documentation
- 🔄 Version controlled

## Requirements

- Python >= 3.7
- pandas >= 1.3.0

## Author

**Dr. Bright Frimpong**  
Assistant Professor, Business Administration  
Williams School of Commerce, Economics, and Politics  
Washington and Lee University

## License

MIT License - Free for educational use

## Updates

To get the latest version:
```bash
pip install --upgrade bus2002
```

## Support

For questions or issues, contact: bfrimpong@wlu.edu
infdb 0.1.1
InfDB wrapper for python
Requires Python: >=3.9

# InfDB Python Wrapper

A tiny helper layer around PostgreSQL for “InfDB” projects. It gives you:

- `InfDB`: one entry point for config + logging + DB connections
- `InfdbClient`: thin psycopg2 client (queries, run SQL files)
- `InfdbConfig`: load/merge YAML config and resolve placeholders
- `InfdbLogger`: stdout + file logging, with worker logger support
- `utils`: misc helpers (env → DSN, atomic writes, quick SQL)

## Install

```bash
uv pip install
```

## Basic idea

1. You write a YAML like `configs/choose-a-name.yml`.
2. You create `InfDB("mytool")`.
3. You call `.connect()` or `.get_db_engine()`.

## Example

```python
from infdb.infdb import InfDB

infdb = InfDB("mytool", config_path="configs")

# psycopg2 client
with infdb.connect("postgres") as db:
    rows = db.execute_query("SELECT now()")
    print(rows)

# SQLAlchemy engine
engine = infdb.get_db_engine("postgres")
with engine.connect() as conn:
    print(conn.execute("SELECT 1").scalar())
```
---
## Functionality documentation

The core package documentation is generated automatically as Markdown pages.  
These pages describe the public API and behavior of each main module in the
`infdb` package.

- **InfDB facade**: [documentation/api/infdb.md](documentation/api/infdb.md)
- **Client**: [documentation/api/client.md](documentation/api/client.md)
- **Config**: [documentation/api/config.md](documentation/api/config.md)
- **Logger**: [documentation/api/logger.md](documentation/api/logger.md)
- **Utils**: [documentation/api/utils.md](documentation/api/utils.md)

### How this documentation is generated

These “functionality” pages are not maintained manually. They are produced by
the documentation automation scripts (API markdown generation → MkDocs build →
export).

The steps and commands to generate/update them are documented here:

- [scripts/README.md](scripts/README.md)
candlestix 2.0.5
Library to get candlesticks info
Requires Python: >=3.8

# candlestix
Python package to fetch candlesticks data for stocks. Focussed on Indian stocks. 

## Build
* Before you build, please ensure you update the project version in `{project-root}/setup.py`
  * The field is `setup -> version`
* To build, run the script `./build.sh` from **project root directory**
  * The build script will automatically delete the following build directories in project root
    * build/
    * dist/
    * candlestix.egg-info/
  * Next, it builds the project using `python3 -m build --wheel`

## Build and Install
* To install run `./build.sh -i` or `./build.sh --install`
* To verify, run `pip list | grep candlestix`

## Uninstall 
- To uninstall, run `pip uninstall candlestix`
- Verify `pip list | grep candlestix`

## Upload 
To upload to PyPi
`twine upload dist/*`

To install twine
`pip install twine`