📅 2023-08-05 — Session: Enhanced Python Data Processing for Poverty Analysis
🕒 22:35–23:25
🏷️ Labels: Python, Data Processing, Poverty Analysis, Automation, Error Handling
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to improve and automate the data processing workflow for poverty analysis using Python, focusing on code refactoring, modularity, and error handling.
Key Activities
- Code Refactoring: Reviewed and refactored data transformation functions to enhance readability, maintainability, and efficiency, particularly using the Pandas library.
- Function Development: Developed functions for calculating basic basket metrics and poverty metrics, ensuring modularity and maintainability.
- Data Merging: Created functions to load and merge CSV files into a single DataFrame based on a common ID, facilitating comprehensive data analysis.
- Automation: Implemented automation for quarterly data processing, including looping through quarters and applying functions to process and save data.
- Error Handling: Incorporated error handling mechanisms to manage file loading exceptions, ensuring robustness in data processing.
Achievements
- Successfully refactored and modularized key functions for poverty data analysis.
- Automated the data processing workflow for the years 2015 and 2016, enhancing efficiency.
- Improved error handling in data loading processes, ensuring continuity in case of missing files.
Pending Tasks
- Further testing and validation of the automated data processing pipeline to ensure accuracy and reliability.
- Exploration of additional data sources to enrich the poverty analysis dataset.