📅 2023-08-05 — Session: Refactored and Automated Poverty Data Analysis
🕒 22:35–23:25
🏷️ Labels: Python, Data Analysis, Refactoring, Automation, Poverty Metrics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance the maintainability and efficiency of Python code used for poverty data analysis by refactoring existing functions and automating data processing workflows.
Key Activities
- Reviewed and refactored a data transformation function to improve readability and efficiency using the Pandas library.
- Defined and refactored Python functions for calculating poverty metrics, focusing on modularity and maintainability.
- Developed functions for loading, merging, and processing CSV data into a single DataFrame for analysis.
- Automated the processing of quarterly data for the years 2015 and 2016, including error handling for missing files.
- Implemented a loop to apply data processing functions across multiple quarters and save the results.
Achievements
- Successfully refactored key functions to improve code clarity and maintainability.
- Automated the quarterly data processing workflow, reducing manual intervention and potential errors.
- Enhanced error handling to ensure robust data processing even with missing files.
Pending Tasks
- Further testing of the automated workflow with additional datasets to ensure robustness.
- Optimization of data processing functions for larger datasets if necessary.