📅 2023-09-12 — Session: Enhanced Graph Data Visualization Techniques
🕒 17:10–17:50
🏷️ Labels: Python, Data Visualization, Graph Theory, Matplotlib, Seaborn
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the visualization of graph data by employing various Python libraries to display and compare graph method performances effectively.
Key Activities
- Displaying Winners Table: Developed Python code using pandas to style and highlight winning methods in tables for sparse and dense graphs.
- Performance Visualization: Created side-by-side plots using Matplotlib and Seaborn to compare graph method performances on different graph types.
- Plot Modifications: Modified plot characteristics in Seaborn, including plot size, grid style, and label translations.
- Time Complexity Analysis: Conducted a theoretical and practical analysis of vertex removal time complexity in different graph data structures.
- LaTeX Formatting in Plots: Implemented LaTeX-style formatting in Matplotlib for better handling of special characters.
Achievements
- Successfully generated styled tables and comparative plots for graph data.
- Enhanced plot aesthetics and clarity through customization.
- Gained insights into time complexity for vertex removal in graphs.
Pending Tasks
- Further exploration of graph method optimizations and their impact on visualization.
- Review and refine LaTeX formatting for broader use cases.