📅 2023-10-24 — Session: Developed Electoral Data Analysis Framework
🕒 20:20–21:50
🏷️ Labels: Electoral Analysis, Data Structuring, Jupyter Notebook, Google Colab, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM
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.