Adapted R and Python Code for Election Data Analysis

📅 2023-10-25 — Session: Adapted R and Python Code for Election Data Analysis

🕒 14:25–15:10
🏷️ Labels: R Programming, Python, Data Analysis, Optimization, Debugging
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to adapt and implement code for analyzing election data using both R and Python, focusing on creating pivot tables, processing data, and applying statistical models.

Key Activities

• Adapted R code for analyzing election data structured in pivot tables, including creating necessary matrices and interpreting results.
• Provided a step-by-step guide for converting Pandas DataFrames to NumPy matrices and implementing optimization functions in Python.
• Implemented the penalized least squares criterion using Python, highlighting differences in indexing and matrix handling between R and Python.
• Utilized Scipy’s minimize function for optimization and created a placeholder for the call_difp function.
• Debugged slicing errors and input issues in Python functions, adding debug print statements for troubleshooting.
• Explained how to add new columns to data frames in both R and Python, and how to normalize voting data using proportions.

Achievements

• Successfully adapted R code for election data analysis and implemented equivalent Python code for data processing and optimization.
• Debugged and improved Python functions for calculating penalized least squares and handling input errors.

Pending Tasks

• Further testing and validation of the adapted Python code to ensure accuracy and efficiency in data analysis.
• Exploration of additional optimization techniques and their impact on the analysis results.