Developed Electoral Data Analysis Framework

• Day: 2023-10-24
• Time: 20:20 to 21:50
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Electoral Analysis, Data Structuring, Jupyter Notebook, Google Colab, Pandas

Description

Session Goal: The primary goal of this session was to design and structure an analytical framework for electoral data analysis, focusing on ecological inference and voter segmentation.

Key Activities:

• Outlined the necessary datasets and their structures for electoral analysis, including ecological inference and voter participation.
• Proposed various notebook structures for analyzing electoral results, covering geographic analysis, candidate performance, and voter behavior.
• Discussed collaborative tools for Jupyter Notebooks, specifically focusing on Google Colab and Visual Studio Code’s Live Share for enhanced teamwork.
• Provided detailed guides on using Google Colab for data handling, including CSV file management and Google Drive integration.
• Reflected on optimizing Pandas code for electoral data analysis, focusing on data transformation and visualization.

Achievements:

• Developed a comprehensive framework for electoral data analysis, including dataset structuring and notebook proposals.
• Enhanced understanding of collaborative tools and their application in data analysis projects.
• Improved efficiency in data handling and code optimization using Pandas in Google Colab.

Pending Tasks:

• Implement the proposed notebook structures and test them with actual electoral datasets.
• Further explore collaborative features in JupyterHub for larger team projects.

Evidence

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• event_ids: []