Enhanced Python Code for Poverty Metrics

📅 2023-08-17 — Session: Enhanced Python Code for Poverty Metrics

🕒 07:00–11:45
🏷️ Labels: Python, Data Processing, Poverty Metrics, DBML, Code Refactoring
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The objective of this session was to enhance and refactor Python code related to poverty metrics, ensuring better data processing, error handling, and database schema design.

Key Activities

• Developed a Python function to transform income data into poverty metrics, including detailed documentation and tracking of data size.
• Suggested descriptive function names to improve code clarity.
• Resolved a TypeError in Python by converting a string to a float.
• Calculated means for float columns in a DataFrame to provide statistical insights.
• Split income data into household and individual datasets, incorporating geographical merges.
• Analyzed datasets for common and unique columns, sizes, and unique values.
• Reviewed functions for consistency and syntax errors, removing unused parameters.
• Refined data processing code for quarterly poverty data, adjusting function arguments and file-saving logic.
• Proposed and corrected a DBML schema for poverty datasets, ensuring accurate foreign key relationships.
• Detailed the function ingresos_de_personas_Q for processing synthetic income data.
• Refactored code for modularity and readability, creating functions for loading and merging data.
• Restructured Python code for efficient processing of population and income data.
• Provided instructions for merging datasets using pandas.

Achievements

• Improved code readability and maintainability through refactoring and modular design.
• Enhanced error handling and data processing logic.
• Established a clear DBML schema for poverty datasets.

Pending Tasks

• Further refinement of the ingresos_de_personas_Q function for performance optimization.
• Additional testing of the DBML schema with real-world datasets.