📅 2024-06-25 — Session: Innovative Economic Theory and Data Processing Enhancements
🕒 13:20–15:40
🏷️ Labels: Economic Theory, Python Scripting, Data Processing, Error Handling, Pareto Optimality
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore innovative approaches to economic theory, focusing on Pareto optimality, and to enhance data processing techniques in Python.
Key Activities
- Economic Theory Exploration: Discussed rewriting economic theory with a focus on Pareto optimality, considering static and intertemporal contexts and non-convex production functions.
- Academic Study Structuring: Outlined a structured approach for an academic economic study, covering problem identification, theoretical development, empirical analysis, political implications, and publication.
- Critical Analysis: Emphasized the importance of critically analyzing public figures in economics, advocating for rigorous debate.
- Python Scripting: Updated a Python script for loading CSV files with a new naming convention, addressing sorted/random data and linear/logarithmic scales.
- Error Handling in DataFrames: Solved a TypeError in DataFrame arithmetic by ensuring numeric-only operations and converting strings to numbers.
- Groupby Error Resolution: Revised a function to handle groupby errors by processing only numeric data, enhancing covariance computation.
- Covariance Calculation Enhancements: Improved a Python function for covariance calculation, ensuring numeric data processing and robust error handling.
Achievements
- Developed a framework for rewriting economic theory with innovative insights.
- Structured a comprehensive plan for an academic economic study.
- Enhanced Python data processing scripts, resolving errors in DataFrame operations and improving covariance calculations.
Pending Tasks
- Further exploration of non-convex production functions in economic theory.
- Finalize and test the revised Python functions for broader data sets.