📅 2023-09-12 — Session: Enhanced Data Visualization with Python Libraries
🕒 18:00–18:35
🏷️ Labels: Data Visualization, Python, Matplotlib, Seaborn, Execution Time
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The primary objective of this session was to enhance data visualization techniques using Python libraries, focusing on execution time visualization across different methods and data structures.
Key Activities:
- Adapted plots to visualize median execution times using Matplotlib and Seaborn, ensuring consistent styling.
- Developed Python code to handle sparse and dense datasets, facilitating method comparison.
- Evaluated and adjusted plot sizes and y-axis settings to improve clarity and comparability.
- Addressed code execution issues by preprocessing data and ensuring all necessary libraries were imported.
- Requested CSV file uploads for further data processing and plotting.
- Implemented logic to determine when to use shared or independent y-axes based on data analysis.
- Debugged y-axis behavior in Matplotlib, focusing on customization and synchronization issues.
Achievements:
- Successfully updated Python code to visualize performance metrics, adjusting y-axis scales based on dataset characteristics.
- Improved plotting techniques by integrating conditions for y-axis customization.
Pending Tasks:
- Finalize the integration of file uploads for complete data processing and visualization.
- Further refine y-axis customization logic to enhance clarity across all plot types.