Metadata-Version: 2.4
Name: kindagooey
Version: 0.1.0
Summary: Digestible, ready-to-read seaborn visualizations for a built-in sample dataset.
Author-email: eeghiz <eeghiz@dons.usfca.edu>
License-Expression: MIT
Project-URL: Homepage, https://github.com/eeghiz-USF/commsSetUpTests
Project-URL: Repository, https://github.com/eeghiz-USF/commsSetUpTests
Keywords: seaborn,visualization,matplotlib,charts,data-visualization
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: seaborn>=0.12
Requires-Dist: matplotlib>=3.5
Requires-Dist: pandas>=1.3
Requires-Dist: numpy>=1.21
Dynamic: license-file

# kindagooey

Gooey, liquid-looking [seaborn](https://seaborn.pydata.org/) visualizations for
a built-in sample dataset. `kindagooey` layers seaborn's palettes with smooth,
flowing curves, glossy "gel" bars, soft glows and gradient pours on a deep
backdrop — so you get an eye-catching, easy-to-read chart in one call.

## Install

```bash
pip install kindagooey
```

## Usage

The package exposes exactly **two functions**, each producing a different
visualization. Call them with no arguments to use the bundled sample dataset
(a week of café drink sales), or pass your own long-form `DataFrame` with
`day`, `drink`, and `units_sold` columns.

```python
import matplotlib.pyplot as plt
import kindagooey

# 1. Ranked bar chart: total units sold per drink
kindagooey.sales_by_category()

# 2. Trend lines: daily units sold per drink across the week
kindagooey.sales_over_week()

plt.show()
```

Both functions return the Matplotlib `Axes`, so you can save or tweak the
result:

```python
ax = kindagooey.sales_by_category()
ax.figure.savefig("weekly_sales.png", dpi=150, bbox_inches="tight")
```

### Quick demo

A ready-to-run script is included. After installing the package, run:

```bash
python run_demo.py
```

It draws both charts, saves them as PNGs, and opens them in a window.

### The sample dataset

A small, deterministic dataset is included so the plots work out of the box:

| day | drink      | units_sold |
|-----|------------|------------|
| Mon | Latte      | 40         |
| ... | ...        | ...        |
| Sat | Latte      | 64         |

## API

| Function | Visualization |
|----------|---------------|
| `sales_by_category(data=None, ax=None)` | Ranked bar chart drawn as glossy, gooey gel capsules (one per drink), value-labelled. |
| `sales_over_week(data=None, ax=None)`   | Weekly trend drawn as smooth liquid streams with a glossy pour beneath. |

## Development

```bash
python -m build                 # build sdist + wheel
python -m twine upload dist/*   # publish to PyPI
```

## License

MIT
