Enhanced Data Visualization for Electoral Analysis

• Day: 2023-11-01
• Time: 03:35 to 04:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data_Analysis, Visualization, Python, Electoral_Data, Matplotlib

Description

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 circuitos and data_circ datasets, 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.

Evidence

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• event_ids: []