Enhanced Python data processing and visualization

Day: 2023-08-22
Time: 19:30 to 21:10
Project: Dev
Workspace: WP 2: Operational
Status: Completed
Priority: MEDIUM
Assignee: Matías Nehuen Iglesias
Tags: Python, Data Processing, Visualization, Elections, Code Optimization

Description

The session aimed to enhance Python data processing capabilities, handle specific error cases, and improve visualization techniques for electoral data analysis.

Handling Empty Arrays in Quantile Computation: A solution was implemented to address errors arising from computing quantiles with empty arrays by adding checks for non-empty arrays before performing calculations.
Modularizing Code for Data Processing: The Python code was optimized by modularizing repeated code into functions and using loops for combinations, specifically for saving data in GeoJSON format.
Análisis de Agrupaciones Electorales por Región: Developed a Python procedure to analyze electoral groupings by region and section, calculating vote percentages for elections in 2019 and 2023.
Generación de Presentaciones en Markdown: Created Python scripts to generate Markdown presentations with tables of votes and percentages organized by region and section for the 2019 and 2023 elections.
Visualización de Votos y Porcentajes por Sección: Implemented a Python script to visualize votes and percentages by section, filtering data to highlight unique combinations and relevant information.

Successfully handled errors related to empty arrays in quantile calculations.
Improved code structure and efficiency through modularization.
Developed comprehensive data analysis and visualization tools for electoral data.

Further testing and validation of the visualization scripts to ensure accuracy and reliability.
Exploration of additional data sets for broader analysis.

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