Rekha: Semantic Plotting for Python¶

Rekha is a modern plotting library that combines an intuitive API with publication-quality output. Create beautiful, customizable visualizations with minimal code.

The Problem¶

Matplotlib is powerful but requires reinventing the wheel for every plot—no semantic interface.

Plotly Express has great semantics but limited customization and poor aesthetics for publications.

Seaborn has good defaults but restrictive API and complicated to use.

The Solution¶

Rekha delivers Plotly Express simplicity with matplotlib’s publication-quality output and unlimited customization, plus modern features like unit humanization and grayscale optimization.

From 15 lines to 1 line:

# Before: Typical matplotlib plotting
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 6))
for category in df['category'].unique():
    data = df[df['category'] == category]
    ax.scatter(data['x'], data['y'], label=category, s=50, alpha=0.7)
ax.set_xlabel('X Values')
ax.set_ylabel('Y Values')
ax.set_title('Scatter Plot by Category')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# After: Rekha plotting
import rekha as rk
fig = rk.scatter(df, x='x', y='y', color='category', title='Scatter Plot by Category')
fig.show()

Key Features¶

🎨 Beautiful Defaults: Pre-configured themes optimized for both light and dark modes
📊 Unified Interface: Consistent API across all plot types with uniform styling options
🖨️ Print-Ready: Built-in support for grayscale printing with patterns and enhanced contrast
⚡ Performance: Optimized for both interactive exploration and production use
🔧 Customizable: Full access to matplotlib’s powerful customization while maintaining simplicity

Quick Start¶

Installation¶

pip install rekha

Basic Usage¶

import rekha as rk
import pandas as pd

# Your data (CSV, database, anywhere)
df = pd.read_csv('your_data.csv')

# One line = beautiful plot
fig = rk.scatter(df, x='column1', y='column2', color='category')
fig.show()

Save and Export¶

# Optimized formats for different use cases
fig.save('plot.svg', format='web')      # Web-ready SVG
fig.save('plot.pdf', format='paper')    # Publication PDF
fig.save('plot.png', format='social')   # High-res PNG