semvec/pypi-packages huggingface dataset:
JSON2CSVLIB 1.0.0
A professional JSON to CSV conversion library
License: MIT
Requires Python: >=3.8
Keywords: json, csv, converter, data, conversion

---

## File: `README.md`
```markdown
# json2csv-pro

A professional, feature-rich Python library for converting JSON data to CSV format with advanced capabilities.

## Features

- ✨ Convert JSON to CSV with ease
- 🔄 Handle nested JSON structures automatically
- 📊 Batch conversion support
- ✅ Data validation
- 🎨 Customizable delimiters and formatting
- 📝 Comprehensive documentation
- 🔍 Type hints for better IDE support
- ⚡ High performance

## Installation

```bash
pip install json2csv-pro
```

## Quick Start

```python
from json2csv_pro import JSONConverter

# Create converter instance
converter = JSONConverter()

# Simple conversion
data = [
    {"name": "John", "age": 30, "city": "NYC"},
    {"name": "Jane", "age": 25, "city": "LA"}
]

converter.convert_to_csv(
    data=data,
    output_file="output.csv"
)
```

## Advanced Usage

### Handling Nested JSON

```python
data = [
    {
        "name": "John",
        "age": 30,
        "address": {
            "city": "NYC",
            "country": "USA"
        }
    }
]

converter.convert_to_csv(
    data=data,
    output_file="output.csv",
    flatten_nested=True,
    delimiter=','
)
```

### Batch Conversion

```python
converter.convert_batch(
    input_files=["file1.json", "file2.json", "file3.json"],
    output_dir="output/",
    flatten_nested=True
)
```

### Preview Before Converting

```python
preview = converter.preview_conversion(
    data=data,
    rows=5
)
print(preview)
```

## Documentation

Full documentation available at: [Read the Docs](https://json2csv-pro.readthedocs.io)

## Contributing

Contributions are welcome! Please read our [Contributing Guide](CONTRIBUTING.md).

## License

MIT License - see [LICENSE](LICENSE) file for details.
```
aesthetic 0.9
a e s t h e t i c
License: MIT
Keywords: astronomy, matplotlib-style-sheets

# a e s t h e t i c

Luke's matplotlib style sheets.
Remixed from John Garrett's [SciencePlots](https://github.com/garrettj403/SciencePlots).

Preferences:
* Avoid constantly repeating matplotlib calls to prettify plots.
* Default font: Arial (not DejaVu Sans, which many systems don't have installed by default)
* Don't draw right and top axis spines unless they provide function.
* White on black reduces eye strain; make generating white on black figures easy by using `*_wob` style sheets.

__install__

_...via pip_: `pip install aesthetic`

_...via local clone_: `git clone git@github.com:lgbouma/aesthetic.git;  cd aesthetic;  pip install -e . `

__examples__

for plot styles, see the [test driver](https://github.com/lgbouma/aesthetic/blob/master/tests/make_test_plot.py).
the general syntax follows:

```
from aesthetic import set_style
set_style("clean")
```

<img src="https://raw.githubusercontent.com/lgbouma/aesthetic/refs/heads/master/results/plot_clean.png?v=2" width="500">


```
set_style("clean_wob")
```

<img src="https://raw.githubusercontent.com/lgbouma/aesthetic/refs/heads/master/results/plot_clean_wob.png?v=2" width="500">

```
set_style("science")
```

<img src="https://raw.githubusercontent.com/lgbouma/aesthetic/refs/heads/master/results/plot_science.png" width="500">

```
set_style("science_wob")
```

<img src="https://raw.githubusercontent.com/lgbouma/aesthetic/refs/heads/master/results/plot_science_wob.png?v=2" width="500">


So if you wanted to make four different versions of the same plot with responsive colors:

```
from aesthetic.plot import set_style

import numpy as np, matplotlib.pyplot as plt, matplotlib as mpl

def make_plot(style):
    x = np.linspace(0,10,1000)
    y = (x/100)**3 + 5*np.sin(x)
    _x, _y = np.arange(2, 8, 0.5), np.arange(2, 8, 0.5)

    fig, ax = plt.subplots(figsize=(3,2.5))
    cs = [None]*3 if '_wob' not in style else ['cyan','lime','yellow']
    ax.plot(x, y, label=f'style: {style}', color=cs[0])
    ax.plot(x, y+3, color=cs[1])
    ax.plot(x, y+6, color=cs[2])
    _yerr = np.abs(np.random.normal(2, 1, _x.size))
    c = 'k' if '_wob' not in style else 'w'
    ax.errorbar(_x, _y, yerr=_yerr, marker='o', elinewidth=0.5, lw=0, c=c, markersize=2)
    ax.update({ 'xlabel': r'x [units]', 'ylabel': r'y [units]' })
    ax.legend(fontsize='small')
    return fig

if __name__ == '__main__':
    styles = ['clean', 'science', 'clean_wob', 'science_wob']
    for style in styles:
        set_style(style)
        fig = make_plot(style)
        fig.savefig(f'../results/plot_{style}.png', bbox_inches='tight', dpi=400)
        mpl.rc_file_defaults()
```