📅 2025-05-28 — Session: Refactored Data Retrieval and Processing Scripts
🕒 06:40–07:10
🏷️ Labels: Data Retrieval, Modular Architecture, Python, Automation, Data Processing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The main goal of this session was to enhance the data retrieval and processing workflows by updating scripts and proposing modular architectures.
Key Activities
- Updated a script for data retrieval to access a new domain and structure, eliminating the need for HTML scraping. This involved verifying URLs and organizing downloadable files into categories.
- Proposed a modular architecture for a linear exploratory notebook to improve reusability, reproducibility, and maintainability.
- Developed scripts for automation, focusing on downloading files, extracting ZIPs, and exporting data from SQLite to CSV.
- Evaluated the
download.pyscript for adherence to the Single Responsibility Principle, enhancing modularity. - Compared zip extraction scripts to highlight improvements in modularity and batch processing capabilities.
- Reviewed and validated scripts for exporting SQLite data to CSV, ensuring functionality replication and suggesting improvements.
- Detailed an automated pipeline for maintaining a local copy of a time series dataset using GitHub Actions.
Achievements
- Successfully updated and evaluated multiple scripts, ensuring they adhere to best practices in modularity and clarity.
- Proposed effective strategies for refactoring and automating data processing workflows.
Pending Tasks
- Implement the proposed modular architecture for the exploratory notebook.
- Further refine and test the automated pipeline for time series datasets.
Outcome
The session resulted in a comprehensive set of updated and new scripts that enhance the efficiency and maintainability of data retrieval and processing tasks.