<div align="center">
# Rekha
*The semantic plotting library for Python with publication-quality output*
[](https://project-vajra.github.io/rekha/)
[](https://discord.gg/wjaSvGgsNN)
[](https://pypi.org/project/rekha/)
---
**One line of code. Publication-ready plots.**
Rekha combines Plotly Express's intuitive API with matplotlib's powerful rendering engine, giving you beautiful visualizations without the boilerplate.
</div>
## ๐ Quick Start
### Installation
```bash
pip install rekha
```
### Basic Usage
```python
import rekha as rk
import pandas as pd
# Your data (any pandas DataFrame)
df = pd.read_csv('your_data.csv')
# One line = beautiful plot
fig = rk.scatter(df, x='x', y='y', color='category')
fig.show()
```
**That's it!** You now have publication-ready visualizations with zero configuration.
## ๐ Sample Graphs
<div align="center">
### Scatter Plot
```python
rk.scatter(df, x='x', y='y', color='species')
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/_static/plots/scatter_colored_dark.png">
<source media="(prefers-color-scheme: light)" srcset="docs/_static/plots/scatter_colored_light.png">
<img src="docs/_static/plots/scatter_colored_light.png" width="450">
</picture>
### Line Plot
```python
rk.line(df, x='date', y='price', color='stock')
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/_static/plots/line_basic_dark.png">
<source media="(prefers-color-scheme: light)" srcset="docs/_static/plots/line_basic_light.png">
<img src="docs/_static/plots/line_basic_light.png" width="450">
</picture>
### Bar Chart
```python
rk.bar(df, x='category', y='sales', color='region')
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/_static/plots/bar_grouped_dark.png">
<source media="(prefers-color-scheme: light)" srcset="docs/_static/plots/bar_grouped_light.png">
<img src="docs/_static/plots/bar_grouped_light.png" width="450">
</picture>
### Heatmap
```python
rk.heatmap(correlation_matrix, text_auto=True)
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/_static/plots/heatmap_correlation_dark.png">
<source media="(prefers-color-scheme: light)" srcset="docs/_static/plots/heatmap_correlation_light.png">
<img src="docs/_static/plots/heatmap_correlation_light.png" width="450">
</picture>
**Every plot type. Same simple API. Beautiful results.**
๐ **[See all plot types in our documentation โ](https://project-vajra.github.io/rekha/)**
</div>
## Why Rekha Exists
**The Plotting Library Dilemma:**
- **Matplotlib** = Powerful but painful. Every plot requires 20+ lines of boilerplate.
- **Plotly Express** = has great semantics but limited customization and poor aesthetics for publications.
- **Seaborn** = Good defaults but restrictive API and complicated to use.
**Rekha's Solution:** Get the best of all worlds. Plotly Express semantics + matplotlib quality + unlimited customization.
## โจ What Makes Rekha Special
๐ฏ **One-Line Plots** - Create publication-ready visualizations with a single function call
๐จ **Intelligent Defaults** - Beautiful themes that work in light mode, dark mode, and print
๐ **All Plot Types** - Line, scatter, bar, histogram, box, heatmap, CDF - unified API
๐จ๏ธ **Print Perfection** - Automatic grayscale optimization with patterns for accessibility
โก **Zero Config** - Works beautifully out of the box, customize when you need to
๐ง **Unlimited Power** - Full matplotlib access when you need advanced customization
## Plot Types
Rekha supports all major statistical visualizations with a unified interface:
- **Line plots** - Time series, trends, multiple series
- **Scatter plots** - Relationships, correlations, bubble charts
- **Bar charts** - Categorical comparisons, grouped/stacked bars
- **Histograms** - Data distributions, density plots
- **Box plots** - Statistical summaries, outlier detection
- **Heatmaps** - 2D data, correlation matrices
- **CDF plots** - Cumulative distributions, percentile analysis
## Key Features
### Smart Export Formats
```python
fig.save('plot.pdf', format='paper') # Publication PDF
fig.save('plot.svg', format='web') # Web-ready SVG
fig.save('plot.png', format='social') # High-res PNG
```
### Theme Support
```python
fig = rk.scatter(df, x='x', y='y', dark_mode=True) # Dark theme
fig = rk.bar(df, x='cat', y='val', grayscale_friendly=True) # Print-ready
```
### Full Matplotlib Access
```python
fig = rk.scatter(df, x='x', y='y')
fig.get_axes()[0].axhline(y=0, color='red', linestyle='--') # Custom elements
```
## ๐ Documentation
Comprehensive documentation is available at: **https://project-vajra.github.io/rekha/**
- **[Quick Start Guide](https://project-vajra.github.io/rekha/quickstart.html)** - Get up and running in minutes
- **[User Guide](https://project-vajra.github.io/rekha/user_guide/)** - Comprehensive feature documentation
- **[API Reference](https://project-vajra.github.io/rekha/api/)** - Complete function reference
## Philosophy
Rekha follows these principles:
1. **Beautiful defaults** - Plots should look professional without tweaking
2. **Simple API** - Common tasks should be one-liners
3. **Full control** - Complex customization should be possible
4. **Performance** - No unnecessary overhead
## ๐ค Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for detailed information on:
- Setting up development environment
- Code style and testing requirements
- Documentation standards
- Pull request process
### Quick Development Setup
```bash
git clone https://github.com/project-vajra/rekha.git
cd rekha
# Install in development mode
make install-dev
# Install pre-commit hooks
pre-commit install
# Verify setup works
make format && make lint && make test
# See all available development commands
make help
```
## Related Projects
Rekha is part of the Project Vajra ecosystem:
- **[Vajra](https://github.com/project-vajra/vajra)** - High-performance inference engine for large language models
- **[Rekha](https://github.com/project-vajra/rekha)** - Beautiful matplotlib visualizations (this project)
## License
Apache License 2.0 - see [LICENSE](LICENSE) file for details.
## Etymology
*Rekha* (เคฐเฅเคเคพ) is a Sanskrit/Hindi word meaning "line", "stroke", or "diagram". It seemed fitting for a library focused on creating beautiful line plots, scatter plots, and other visualizations.
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"description": "<div align=\"center\">\n\n# Rekha\n\n*The semantic plotting library for Python with publication-quality output*\n\n[](https://project-vajra.github.io/rekha/)\n[](https://discord.gg/wjaSvGgsNN)\n[](https://pypi.org/project/rekha/)\n\n---\n\n**One line of code. Publication-ready plots.**\n\nRekha combines Plotly Express's intuitive API with matplotlib's powerful rendering engine, giving you beautiful visualizations without the boilerplate.\n\n</div>\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install rekha\n```\n\n### Basic Usage\n\n```python\nimport rekha as rk\nimport pandas as pd\n\n# Your data (any pandas DataFrame)\ndf = pd.read_csv('your_data.csv')\n\n# One line = beautiful plot\nfig = rk.scatter(df, x='x', y='y', color='category')\nfig.show()\n```\n\n**That's it!** You now have publication-ready visualizations with zero configuration.\n\n## \ud83d\udcca Sample Graphs\n\n<div align=\"center\">\n\n### Scatter Plot\n```python\nrk.scatter(df, x='x', y='y', color='species')\n```\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/_static/plots/scatter_colored_dark.png\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"docs/_static/plots/scatter_colored_light.png\">\n <img src=\"docs/_static/plots/scatter_colored_light.png\" width=\"450\">\n</picture>\n\n### Line Plot \n```python\nrk.line(df, x='date', y='price', color='stock')\n```\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/_static/plots/line_basic_dark.png\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"docs/_static/plots/line_basic_light.png\">\n <img src=\"docs/_static/plots/line_basic_light.png\" width=\"450\">\n</picture>\n\n### Bar Chart\n```python\nrk.bar(df, x='category', y='sales', color='region')\n```\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/_static/plots/bar_grouped_dark.png\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"docs/_static/plots/bar_grouped_light.png\">\n <img src=\"docs/_static/plots/bar_grouped_light.png\" width=\"450\">\n</picture>\n\n### Heatmap\n```python\nrk.heatmap(correlation_matrix, text_auto=True)\n```\n<picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/_static/plots/heatmap_correlation_dark.png\">\n <source media=\"(prefers-color-scheme: light)\" srcset=\"docs/_static/plots/heatmap_correlation_light.png\">\n <img src=\"docs/_static/plots/heatmap_correlation_light.png\" width=\"450\">\n</picture>\n\n**Every plot type. Same simple API. Beautiful results.**\n\n\ud83d\udcd6 **[See all plot types in our documentation \u2192](https://project-vajra.github.io/rekha/)**\n\n</div>\n\n## Why Rekha Exists\n\n**The Plotting Library Dilemma:**\n\n- **Matplotlib** = Powerful but painful. Every plot requires 20+ lines of boilerplate.\n- **Plotly Express** = has great semantics but limited customization and poor aesthetics for publications.\n- **Seaborn** = Good defaults but restrictive API and complicated to use.\n\n**Rekha's Solution:** Get the best of all worlds. Plotly Express semantics + matplotlib quality + unlimited customization.\n\n## \u2728 What Makes Rekha Special\n\n\ud83c\udfaf **One-Line Plots** - Create publication-ready visualizations with a single function call \n\ud83c\udfa8 **Intelligent Defaults** - Beautiful themes that work in light mode, dark mode, and print \n\ud83d\udcca **All Plot Types** - Line, scatter, bar, histogram, box, heatmap, CDF - unified API \n\ud83d\udda8\ufe0f **Print Perfection** - Automatic grayscale optimization with patterns for accessibility \n\u26a1 **Zero Config** - Works beautifully out of the box, customize when you need to \n\ud83d\udd27 **Unlimited Power** - Full matplotlib access when you need advanced customization\n\n## Plot Types\n\nRekha supports all major statistical visualizations with a unified interface:\n\n- **Line plots** - Time series, trends, multiple series\n- **Scatter plots** - Relationships, correlations, bubble charts \n- **Bar charts** - Categorical comparisons, grouped/stacked bars\n- **Histograms** - Data distributions, density plots\n- **Box plots** - Statistical summaries, outlier detection\n- **Heatmaps** - 2D data, correlation matrices\n- **CDF plots** - Cumulative distributions, percentile analysis\n\n## Key Features\n\n### Smart Export Formats\n```python\nfig.save('plot.pdf', format='paper') # Publication PDF\nfig.save('plot.svg', format='web') # Web-ready SVG \nfig.save('plot.png', format='social') # High-res PNG\n```\n\n### Theme Support\n```python\nfig = rk.scatter(df, x='x', y='y', dark_mode=True) # Dark theme\nfig = rk.bar(df, x='cat', y='val', grayscale_friendly=True) # Print-ready\n```\n\n### Full Matplotlib Access\n```python\nfig = rk.scatter(df, x='x', y='y')\nfig.get_axes()[0].axhline(y=0, color='red', linestyle='--') # Custom elements\n```\n\n## \ud83d\udcd6 Documentation\n\nComprehensive documentation is available at: **https://project-vajra.github.io/rekha/**\n\n- **[Quick Start Guide](https://project-vajra.github.io/rekha/quickstart.html)** - Get up and running in minutes\n- **[User Guide](https://project-vajra.github.io/rekha/user_guide/)** - Comprehensive feature documentation\n- **[API Reference](https://project-vajra.github.io/rekha/api/)** - Complete function reference\n\n## Philosophy\n\nRekha follows these principles:\n\n1. **Beautiful defaults** - Plots should look professional without tweaking\n2. **Simple API** - Common tasks should be one-liners\n3. **Full control** - Complex customization should be possible\n4. **Performance** - No unnecessary overhead\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for detailed information on:\n\n- Setting up development environment\n- Code style and testing requirements\n- Documentation standards\n- Pull request process\n\n### Quick Development Setup\n\n```bash\ngit clone https://github.com/project-vajra/rekha.git\ncd rekha\n\n# Install in development mode\nmake install-dev\n\n# Install pre-commit hooks\npre-commit install\n\n# Verify setup works\nmake format && make lint && make test\n\n# See all available development commands\nmake help\n```\n\n## Related Projects\n\nRekha is part of the Project Vajra ecosystem:\n\n- **[Vajra](https://github.com/project-vajra/vajra)** - High-performance inference engine for large language models\n- **[Rekha](https://github.com/project-vajra/rekha)** - Beautiful matplotlib visualizations (this project)\n\n## License\n\nApache License 2.0 - see [LICENSE](LICENSE) file for details.\n\n## Etymology\n\n*Rekha* (\u0930\u0947\u0916\u093e) is a Sanskrit/Hindi word meaning \"line\", \"stroke\", or \"diagram\". 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