📅 2023-11-01 — Session: Developed Data Visualization for Voting Analysis
🕒 19:20–20:50
🏷️ Labels: Data Visualization, Python, Seaborn, Matplotlib, Voting Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques for analyzing voting patterns across different income levels and political parties using Python libraries such as Pandas, Seaborn, and Matplotlib.
Key Activities
- Identified Column Differences: Used set operations to identify differences in column names across multiple DataFrames.
- Data Manipulation: Added cumulative percentage variables to the ‘circuitos’ table for electors by number and income.
- Boxplot Creation: Developed a Seaborn boxplot to visualize voting percentages across income levels, with enhancements for color mapping and aesthetics.
- Aesthetic Enhancements: Improved plot aesthetics by adjusting themes, color palettes, and font settings.
- Code Environment Management: Reset the code execution environment and re-imported necessary packages.
- Data Management: Requested re-upload of essential datasets and identified issues with undefined datasets.
- Subplot Creation: Created subplots to visualize voting patterns for multiple political parties.
Achievements
- Successfully visualized voting patterns using boxplots and subplots, enhancing the understanding of voting trends across different demographics.
- Addressed technical warnings and improved the aesthetic quality of the visualizations.
Pending Tasks
- Define the ‘data_filtered’ dataset to ensure all visualizations can be executed without errors.
- Re-upload missing datasets (‘circuitos.csv’ and ‘votos.csv’) to restore full functionality.