Developed Modular Weighted Box Plot Functions in Python

• Day: 2023-08-18
• Time: 19:45 to 20:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Visualization, Modular Programming, Weighted Box Plot

Description

Session Goal

The session focused on enhancing [[data visualization]] techniques by developing modular functions for creating weighted box plots in Python.

Key Activities

• Created a step-by-step guide to generate weighted box plots using custom functions for weighted quartiles computation.
• Implemented a methodology for computing weighted quartiles by bins using Python, leveraging Pandas for data manipulation.
• Developed custom boxplot visualization techniques with Matplotlib, detailing visual elements.
• Implemented plot_scatter and plot_weighted_box functions for generating scatter and weighted box plots, respectively.
• Refactored the plot_scatter function to improve modularity and reusability by accepting parameters directly.
• Created a color dictionary for political groups to enhance plot aesthetics.
• Modularized the plot_weighted_box function by adding arguments for data input and binning, updating the code for weighted quantiles and box plot generation.

Achievements

• Successfully developed and refactored functions for creating modular and reusable [[data visualization]] components.
• Enhanced the flexibility and usability of plotting functions with parameterized inputs.

Pending Tasks

• Further testing and validation of the modular functions with diverse datasets to ensure robustness and accuracy in different scenarios.

Evidence

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• event_ids: []