📅 2023-10-24 — Session: Developed Electoral Data Analysis Framework
🕒 20:20–21:50
🏷️ Labels: Electoral Analysis, Data Structuring, Collaboration Tools, Pandas Optimization
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop a comprehensive framework for analyzing electoral data, focusing on ecological inference and voter segmentation.
Key Activities
- Data Analysis Planning: Identified necessary datasets and proposed analyses for electoral results, including ecological inference and voter segmentation.
- Data Structuring: Outlined the structure of datasets required for electoral analysis, covering voter participation and socioeconomic indicators.
- Notebook Proposals: Developed ideas for notebooks to analyze electoral results, including geographic and candidate performance analysis.
- Collaborative Tools Exploration: Reviewed tools for collaborative work on Jupyter Notebooks, such as VS Code Live Share, Google Colab, and JupyterHub.
- Google Colab Instructions: Provided guides on managing files in Google Colab, including CSV handling and Google Drive integration.
- Pandas Optimization: Reflected on code structure for electoral analysis using Pandas, emphasizing optimization and documentation.
Achievements
- Established a clear framework for conducting electoral data analysis, including the use of ecological inference.
- Created a structured approach for developing analysis notebooks, enhancing collaboration and data handling.
- Improved understanding of collaborative tools and data management in Google Colab.
Pending Tasks
- Implement the proposed notebook structures in actual data analysis projects.
- Further explore collaborative features in JupyterHub for large-scale projects.
- Continue optimizing data handling processes in Pandas for efficiency.