📅 2023-11-02 — Session: Enhanced Data Visualization with Matplotlib
🕒 03:00–03:30
🏷️ Labels: Matplotlib, Seaborn, Data Visualization, Python, Annotations
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques using Matplotlib, focusing on customizing boxplots, conditional x-axis adjustments, and adding annotations to graphs.
Key Activities
- Developed a solution for customizing the placement of boxplots in Matplotlib to overcome Seaborn’s limitations, including handling missing data points.
- Created Python code snippets to conditionally set x-axis ticks and labels in Matplotlib based on row indices in subplots.
- Added annotations and epigraphs to graphs using Matplotlib and Seaborn, with a focus on highlighting voting trends in the AMBA region.
- Provided a structured epigraph template for analyzing vote distribution by income and party in Argentina.
- Adjusted graph sizes and spacing in Matplotlib and Seaborn for improved data visualization.
Achievements
- Successfully implemented customized boxplot placements and conditional x-axis settings.
- Enhanced graphs with annotations and epigraphs to convey complex data insights.
- Improved overall graph aesthetics and clarity through size and spacing adjustments.
Pending Tasks
- Further exploration of advanced data visualization techniques in Matplotlib and Seaborn to continue enhancing the clarity and impact of visual data presentations.