Enhanced and Customized Voting Pattern Boxplots

📅 2023-11-01 — Session: Enhanced and Customized Voting Pattern Boxplots

🕒 19:20–20:50
🏷️ Labels: Python, Data Visualization, Seaborn, Matplotlib, Boxplot, Voting Patterns
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to enhance and customize data visualizations, specifically boxplots, to analyze voting patterns across different income levels and political parties using Python libraries such as Seaborn and Matplotlib.

Key Activities

• Identified Column Differences: Utilized set operations to find differences in column names across multiple DataFrames.
• Data Manipulation: Added cumulative percentage variables to the circuitos table for a more comprehensive analysis of elector data.
• Boxplot Creation and Customization: Developed boxplots to visualize voting patterns, addressing color mapping issues and enhancing plot aesthetics through theme settings, color palettes, and font adjustments.
• Environment Management: Restored the code execution environment and requested re-upload of essential datasets due to reset.
• Dataset Management: Identified the need for a data_filtered dataset and requested its definition for further analysis.
• Deprecation Warning Fix: Resolved a Matplotlib deprecation warning by adopting Seaborn’s styling functions.

Achievements

• Successfully created and customized boxplots for visualizing voting patterns, incorporating aesthetic improvements and resolving technical warnings.
• Improved data analysis capabilities by adding new variables and managing datasets effectively.

Pending Tasks

• Dataset Preparation: Define or generate the data_filtered dataset to enable further analysis and visualization.
• Data Re-upload: Ensure re-upload of circuitos.csv and votos.csv datasets to maintain analysis continuity.