Adapted R and Python code for data analysis

• Day: 2023-10-25
• Time: 14:20 to 15:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: R, Python, Data Analysis, Optimization, Debugging

Description

Session Goal

The session aimed to adapt and implement data analysis techniques in both R and Python, focusing on election data and optimization methods.

Key Activities

• Adapted R code for analyzing election data using pivot tables.
• Converted Pandas DataFrames to NumPy matrices in Python for optimization tasks.
• Implemented the penalized least squares criterion in Python, translated from R.
• Utilized Scipy’s minimize function for optimization, creating a Python equivalent of the params_estim function.
• Debugged slicing errors and input issues in Python functions, enhancing error handling and validation.
• Added new columns to data frames in R and Python, and normalized voting data using proportions.

Achievements

• Successfully adapted R code to Python for data analysis and optimization tasks.
• Implemented and debugged key functions, ensuring proper data handling and error management.

Pending Tasks

• Further testing and validation of the adapted functions to ensure robustness in various data scenarios.
• Optimization of the code for performance improvements, particularly in large datasets.

Evidence

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• event_ids: []