Refactored and Optimized Python Data Scripts
- Day: 2023-12-20
- Time: 13:20 to 14:55
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Python, Data Processing, Code Refactoring, Optimization, Jupyter Notebooks
Description
Session Goal:
The session aimed to refactor and optimize Python scripts related to data processing and visualization, specifically focusing on improving readability, maintainability, and efficiency.
Key Activities:
- Revised a prompt for Jupyter Notebook analysis to enhance code functionality and integration.
- Proposed a restructuring plan for economic network analysis notebooks to enhance modularity and coherence.
- Refactored Python code for data processing using Dask and Pandas to improve maintainability.
- Streamlined [[data visualization]] notebooks, focusing on degree distribution plots with Pandas and Matplotlib.
- Debugged and corrected code for plotting degree distributions, addressing runtime warnings and errors.
- Improved the structure of data preparation scripts for better organization and modularization.
- Developed efficient methods for counting rows in large data files using Python and Bash.
- Added comments to Python code cells to enhance readability and understanding.
- Modularized large data processing scripts into smaller functions for better clarity and reuse.
Achievements:
- Enhanced the readability and maintainability of Python scripts through refactoring and modularization.
- Successfully debugged and optimized [[data visualization]] and processing scripts.
- Improved the structure and coherence of Jupyter notebooks for economic analysis.
Pending Tasks:
- Further testing and validation of refactored scripts in different environments.
- Implementation of the proposed restructuring plan for economic network analysis notebooks.
Evidence
- source_file=2023-12-20.sessions.jsonl, line_number=2, event_count=0, session_id=540992981ef125bfb5e5b2fd355b02da90179bd4be93a575bdd2043bbc31708c
- event_ids: []