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, Dataframes, Numpy
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the efficiency and accuracy of Python functions used in data analysis and optimization tasks.

Key Activities

• Merged two pandas DataFrames, mesas and dsGpre, ensuring proper alignment by resetting indices and specifying merge keys.
• Debugged the params_estim and call_difp functions to address dimension mismatches and optimize performance, adding print statements for better diagnostics.
• Adjusted the parSeed parameter using an identity matrix in NumPy, ensuring correct initialization for optimization problems.
• Modified a function to include data sampling capabilities, enhancing flexibility in handling large datasets.
• Conducted a code review and updated parameter estimation functions with detailed comments and testing examples.

Achievements

• Successfully merged DataFrames and debugged key functions, improving their reliability and performance.
• Enhanced the initialization of optimization parameters, ensuring compatibility with model requirements.
• Improved code clarity and functionality through a comprehensive code review.

Pending Tasks

• Further testing of the modified functions with larger datasets to ensure scalability and robustness.
• Explore additional optimization techniques to enhance performance further.