ggviews 0.2.0

ggplot2 interface to holoviews

ggviews: A ggplot2-style API for holoviews

ggviews is a comprehensive Python library that brings the beloved ggplot2 grammar of graphics to Python, built on top of holoviews for interactive visualizations. Create publication-quality plots with the intuitive, layered syntax that R users know and love.

✨ Recent Major Updates (v0.3.0)

• 🎯 AUTOMATIC LEGENDS: Just like ggplot2 - aes(color='species') now creates legends automatically
• 📊 STATISTICAL GEOMS: geom_boxplot(), geom_density() for comprehensive analysis
• 🌈 COLORBREWER INTEGRATION: Professional publication palettes with scale_colour_brewer()
• 🎛️ ADVANCED THEMING: theme() with element_text(), element_blank() for fine control
• 🗺️ GEOGRAPHIC MAPPING: geom_map() with multiple projections and features
• 🔄 COORDINATE SYSTEMS: coord_flip() for horizontal layouts
• 🔥 2D VISUALIZATION: geom_tile(), geom_raster() for heatmaps and image data
• 🔧 BUG FIXES: Resolved toolbar duplication, improved notebook display, fixed smooth placement

🎯 60% ggplot2 Coverage Achieved!

Features

✨ Familiar Syntax: Use the same grammar of graphics you know and love from ggplot2 🔗 Method Chaining: Build plots incrementally with intuitive method chaining
🎨 Rich Geoms: Support for all major geom types (point, line, bar, histogram, etc.) 🎭 Themes: Beautiful default themes matching ggplot2's aesthetics 🎯 Faceting: Powerful facet_wrap and facet_grid for subplotting ⚡ Interactive: Leverage holoviews' interactive capabilities 📊 Extensible: Easy to extend with custom geoms and themes

Installation

pip install ggviews

For development installation:

git clone https://github.com/ggviews/ggviews.git
cd ggviews  
pip install -e ".[dev]"

Quick Start

from ggviews import ggplot, aes
import pandas as pd

# Load your data
df = pd.read_csv('your_data.csv')

# Create a beautiful plot with method chaining
(ggplot(df, aes(x='height', y='weight'))
 .geom_point(aes(color='species'), size=3, alpha=0.7)
 .geom_smooth(method='lm')
 .theme_minimal()
 .labs(
     title='Height vs Weight by Species',
     x='Height (cm)', 
     y='Weight (kg)'
 )
 .facet_wrap('~species'))

Core Concepts

Grammar of Graphics

ggviews implements the layered grammar of graphics:

1. Data: Your pandas DataFrame
2. Aesthetics: Map data to visual properties (x, y, color, size, etc.)
3. Geometries: Visual representations of data (points, lines, bars, etc.)
4. Scales: Control aesthetic mappings
5. Themes: Overall visual styling
6. Facets: Subplots based on data subsets

Method Chaining

Build plots incrementally:

plot = (ggplot(data)
        .geom_point()     # Add points
        .geom_line()      # Add lines  
        .theme_classic()  # Apply theme
        .labs(title='My Plot'))  # Add labels

Available Geoms

• geom_point() - Scatter plots
• geom_line() - Line plots
• geom_bar() - Bar charts
• geom_histogram() - Histograms
• geom_boxplot() - Box plots
• geom_violin() - Violin plots
• geom_density() - Density plots
• geom_smooth() - Smoothed conditional means
• geom_area() - Area plots
• geom_ribbon() - Ribbons/confidence bands
• geom_tile() - Heatmaps
• geom_text() - Text annotations

Themes

• theme_minimal() - Clean, minimal theme
• theme_classic() - Classic ggplot2 theme
• theme_bw() - Black and white theme
• theme_dark() - Dark theme
• theme_void() - Completely blank theme

Scales

• scale_color_manual() - Manual color scales
• scale_color_discrete() - Discrete color scales
• scale_color_continuous() - Continuous color scales
• scale_x_continuous() / scale_y_continuous() - Continuous axis scales
• scale_x_discrete() / scale_y_discrete() - Discrete axis scales

Faceting

Create subplots with:

• facet_wrap('~variable') - Wrap subplots in a grid
• facet_grid('row_var ~ col_var') - Grid of subplots

Examples

Basic Scatter Plot

ggplot(df, aes(x='x', y='y')).geom_point()

Colored by Category

ggplot(df, aes(x='x', y='y', color='category')).geom_point()

Multiple Layers

(ggplot(df, aes(x='x', y='y'))
 .geom_point()
 .geom_smooth())

Faceted Plot

(ggplot(df, aes(x='x', y='y'))
 .geom_point() 
 .facet_wrap('~group'))

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Inspired by Hadley Wickham's ggplot2
• Built on the excellent holoviews library
• Thanks to the broader Python data visualization community