📅 2023-09-12 — Session: Enhanced graph visualization and analysis techniques
🕒 17:10–17:50
🏷️ Labels: Python, Data Visualization, Graph Theory, Matplotlib, Seaborn
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The goal of this session was to enhance the visualization and analysis of graph data using Python, focusing on both sparse and dense graphs.
Key Activities:
- Displaying Winners Table: Modified Python code to display winners tables for sparse and dense graphs using pandas, with styling to highlight winning methods.
- Side-by-Side Plots: Generated side-by-side plots using Matplotlib and Seaborn to compare the performance of various graph methods.
- Plot Modifications: Adjusted plot characteristics in Seaborn, including plot size reduction, grid addition, and translation of labels to Spanish.
- Time Complexity Analysis: Analyzed the time complexity of vertex removal in graph data structures, comparing edge list and adjacency matrix approaches.
- LaTeX in Matplotlib: Implemented LaTeX-style formatting in Matplotlib to handle special characters correctly.
Achievements:
- Successfully enhanced the visualization of graph data with improved styling and comparison plots.
- Gained insights into the time complexity of vertex removal in different graph representations.
Pending Tasks:
- Further exploration of alternative graph visualization techniques and their impact on performance analysis.