📅 2023-11-01 — Session: Enhanced Data Visualization for Electoral Analysis
🕒 03:35–04:35
🏷️ Labels: Data_Analysis, Visualization, Python, Electoral_Data, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to improve data processing and visualization techniques for analyzing electoral data, specifically focusing on income data and voter behavior.
Key Activities
- Reviewed code snippets for processing and merging datasets to analyze income data and voter behavior.
- Outlined steps for merging
circuitosanddata_circdatasets, applying binning, and creating visualizations. - Requested the upload of necessary datasets for further analysis.
- Developed a structured workflow for merging and analyzing electoral circuit datasets.
- Adapted Python code for visualizing political data using box plots and scatter plots.
- Simplified plotting code to enhance clarity and reduce complexity.
- Implemented visualization techniques for political votes and income levels, including scatter plots and weighted box plots.
- Modified plot aesthetics in Matplotlib, including color adjustments and error handling for font size parameters.
Achievements
- Successfully outlined and executed a comprehensive workflow for merging and analyzing datasets.
- Enhanced data visualization techniques using Python libraries such as Matplotlib and Seaborn.
- Improved code clarity and reduced complexity in plotting scripts.
Pending Tasks
- Ensure the uploaded datasets are correctly integrated into the workflow for further analysis.
- Continue refining visualization aesthetics and error handling for future sessions.