📅 2023-10-02 — Session: Refactored Python functions for modular data analysis
🕒 13:20–15:50
🏷️ Labels: Python, Refactoring, Data Analysis, Modularity, Notebooks
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the modularity and maintainability of a data analysis project by refactoring Python functions, organizing notebooks, and addressing common Python errors.
Key Activities
- Markdown Commenting Shortcuts: Explored keyboard shortcuts for commenting in various editors, noting limitations in online Markdown previewers.
- Notebook Organization: Provided insights and suggestions for organizing and optimizing notebooks, focusing on modularity and documentation.
- Modular Structure Planning: Outlined a modular structure for a data analysis project, detailing code organization into logical units for better maintenance and reuse.
- Error Handling in Python: Addressed
ImportErrorandNameErrorissues, providing solutions for troubleshooting and modifying functions to accept external arguments. - Code Review and Refactoring: Conducted a code review, suggesting improvements for data loading functions, and recommended refactoring functions to enhance reusability by accepting global variables as arguments.
- Data Collection Workflow: Described a workflow for collecting and processing socioeconomic statistics in Argentina, including data sources, methodologies, and data processing stages.
Achievements
- Successfully refactored functions to improve reusability and clarity.
- Developed a structured plan for organizing code and notebooks in a modular fashion.
- Identified and resolved common Python errors, enhancing code reliability.
Pending Tasks
- Implement the proposed modular structure in the data analysis project.
- Continue refining the documentation and organization of notebooks for optimal performance.