📅 2023-09-12 — Session: Enhanced Data Visualization for Execution Time Analysis
🕒 18:00–18:35
🏷️ Labels: Data Visualization, Python, Matplotlib, Seaborn, Execution Time
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques to effectively analyze and compare execution times of various methods across different data structures using Python libraries.
Key Activities
- Adapted plots to visualize median execution times using Matplotlib and Seaborn.
- Implemented Python code for plotting sparse and dense datasets, ensuring consistent styling for comparability.
- Adjusted plot sizes and evaluated execution times to decide on the use of shared or independent y-axes.
- Addressed code execution issues by reloading and preprocessing data, and resolved library import oversights.
- Requested file uploads for processing and plotting, emphasizing the need for CSV files.
- Developed logic to determine when to use shared versus independent y-axes based on data analysis.
- Updated Python code to adjust y-axis scales according to performance metrics.
- Debugged y-axis behavior in Matplotlib, focusing on the
shareyparameter andax.set_ylim()function. - Customized y-axis behavior in plotting loops to track maximum y-values for shared y-axis methods.
Achievements
- Successfully visualized performance metrics for various graph methods on sparse and dense graphs.
- Enhanced understanding of y-axis customization and control in Matplotlib subplots.
Pending Tasks
- Further refinement of y-axis logic to improve clarity in performance comparisons.
- Integration of additional datasets once CSV files are provided.