Key Features and Customization Options for Circular Plots

Circular Visualization Implementation using pyCirclize

Circular Layouts: Suitable for genomic data, network data, and hierarchical data.
Customization: Users can specify colors, sizes, labels, and other graphical elements.
Data Integration: Supports data from Pandas DataFrames, NumPy arrays, and networkx graphs.
Partitioning: Allows partitioning the circular plot into sectors for different datasets or categories.
Data Annotation: Users can add text, lines, or other graphical elements to highlight specific features.
Interactive Features: Includes hover tooltips and zooming capabilities.
Efficiency: Designed to handle large datasets efficiently.
High-Quality Output: Suitable for publication or presentation, with export options in PNG, PDF, and SVG formats.

Visualizing Data with pyCirclize: A Guide to Circular Plots

PyCirclize is a versatile Python package designed for creating eye-catching circular visualizations. Inspired by the R package “circlize”, it leverages the capabilities of matplotlib to generate various circular plots, including Circos Plots, Chord Diagrams, and Radar Charts.

In this article, we will implement examples using pyCirclize to demonstrate its capabilities in creating circular visualizations. We’ll cover everything from the basics of installing pyCirclize to advanced use cases like human genome Circos plots and circular heatmaps.

Table of Content

Understanding Circular Visualization
Circular Visualization Implementation using pyCirclize

Installation pyCirclize in Python
Example 1: Circular Visualization Layout
Example 2: Visualizing Data on Circular Track
Example 3: Link Visualization Between Circular Plot
Example 4: Chord Diagram from Matrix
Example 5: Chord Diagram from From-To Table
Example 6: Human Genome Circos Plot
Example 7: Circular Histogram Visualization
Example 8: Circular Heatmap in Python