📅 2023-08-17 — Session: Refactored and Documented Poverty Data Processing Functions
🕒 07:05–11:45
🏷️ Labels: Python, Data Processing, Poverty Metrics, DBML, Code Refactoring
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance and document Python functions for processing income data into poverty metrics, ensuring modularity, clarity, and efficiency.
Key Activities
- Developed a Python function to transform income data into poverty metrics, including comprehensive documentation and tracking mechanisms.
- Suggested descriptive function names to improve clarity in application development.
- Resolved a TypeError related to integer division by converting a string to a float.
- Calculated mean values for float columns in a DataFrame to provide statistical insights.
- Prepared data by splitting income datasets and incorporating geographical merges.
- Analyzed datasets to identify common and unique columns, sizes, and unique values.
- Reviewed functions for consistency, syntax errors, and improved documentation.
- Refined Python scripts for quarterly poverty data processing, adjusting arguments and file-saving logic.
- Proposed and corrected DBML schema for poverty datasets, emphasizing correct foreign key relationships.
- Refactored code to enhance modularity, readability, and maintainability.
- Restructured Python code to efficiently process synthetic population and income data.
- Defined DBML structure for poverty datasets, including table definitions and relationships.
- Merged datasets using pandas with examples of inner and outer merges.
Achievements
- Successfully documented and refined functions for poverty data processing.
- Improved code modularity and readability through refactoring.
- Established a clear DBML schema for poverty datasets.
- Enhanced data analysis capabilities with statistical insights.
Pending Tasks
- Further review and testing of the refactored functions to ensure robustness.
- Additional documentation for newly created functions to maintain clarity.