Debugged and optimized Python functions for data analysis

📅 2023-10-25 — Session: Debugged and optimized Python functions for data analysis

🕒 15:25–16:30
🏷️ Labels: Python, Optimization, Debugging, Data Manipulation
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to debug and optimize several Python functions related to data manipulation and optimization processes.

Key Activities

• Merging DataFrames: Implemented a step-by-step guide to merge two pandas DataFrames, ensuring proper alignment by resetting indices and specifying merge keys.
• Debugging Functions: Enhanced the params_estim and call_difp functions with additional debugging print statements to diagnose dimension mismatches and optimize function performance.
• Parameter Initialization: Adjusted the parSeed parameter using a matrix identity and corrected its initialization as a column of ones.
• Function Modification: Modified a function to include a frac parameter for data sampling, improving flexibility in data handling.
• Code Review: Conducted a code review and updated functions for parameter estimation, adding detailed comments and testing examples.

Achievements

• Successfully merged DataFrames with correct alignment.
• Improved debugging processes for function optimization.
• Correctly initialized parameters for optimization models.
• Enhanced data sampling capabilities in function design.
• Completed a thorough code review and updated parameter estimation functions for clarity and performance.

Pending Tasks

• Further testing of the modified functions in different scenarios to ensure robustness.
• Explore additional optimization techniques for parameter estimation.