semvec/pypi-packages huggingface dataset:
sqlsynthgen 0.4.2
Synthetic SQL data generator
License: MIT
Requires Python: >=3.9,<3.11

# sqlsynthgen

[![python](https://img.shields.io/badge/Python-3.9|3.10-3776AB.svg)](https://www.python.org)
[![PyPi version](https://badgen.net/pypi/v/sqlsynthgen/)](https://pypi.org/project/sqlsynthgen)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)

Synthetic data for SQL databases

## Documentation

Welcome to `sqlsynthgen`.
Our full documentation is available on <https://sqlsynthgen.readthedocs.io/>.

## Development

Please check the [CONTRIBUTING.md](CONTRIBUTING.md) file.
timefile 0.2.0
Probably the simplest time profiling in python
License: MIT
Requires Python: >=3.9,<4.0

# ⏱️ timefile
Probably the simplest time profiling in python 

## 📍 Getting started
```
pip install timefile
```

To time your functions simply import <code>watch</code> like this:
```python
from timefile import watch
import time

@watch
def o_one(n):
    time.sleep(n/10**4)

@watch
def o_two(m):
    time.sleep(m**2/10**4)

for i in range(10):
    o_one(i)
    o_two(i)
```
Check out the plots saved in the **timefiles/plots/** directory for visualizations of the runtime.

## 🍻 Feedback and Contributions
I warmly welcome any contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas.
Feel free to create issues or pull requests at the [timefile GitHub](https://github.com/hugofe99/timefile).

## 🛣️ Roadmap
 - [ ] Tests !!!
 - [ ] Documentation
 - [ ] Small fixes / cleanup
 - [ ] Performance improvements
 - [ ] Prettier plots
 - [ ] Multivariable plots?
 - [ ] Simple regression or analysis? 

___

#### ⚠️ Disclaimer 
This package is designed for simple runtime insights and is **not** optmized for performance. For more comprehensive profiling in Python, consider using more robust tools like cProfile, timeit, line_profiler, etc.