Enhanced Data Visualization with Python and Matplotlib

• Day: 2023-10-26
• Time: 21:40 to 22:25
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Visualization, Matplotlib, Pandas, Percentiles

Description

Session Goal

The session aimed to enhance [[data visualization]] techniques using Python and Matplotlib, focusing on household and individual datasets.

Key Activities

• Developed Python scripts to plot household and individual datasets with distinct markers using Matplotlib.
• Implemented features to shade colors based on AGLOSI values and added a secondary y-axis.
• Created plots for multiple observables, including ‘P47T_hogar’ and ‘P47T_persona’, with marker customization and moving averages.
• Calculated and visualized 25th and 75th percentiles directly from datasets.
• Filtered datasets based on quantiles and visualized results with area filling between percentiles.
• Applied rolling averages to percentiles and visualized median income data.
• Created side-by-side plots for ‘Hogares’ and ‘Hogares Indigentes’ with color coding and moving averages.
• Modified plots to include grids and set y-axis limits for better visualization of poverty metrics.

Achievements

• Successfully implemented advanced [[data visualization]] techniques, enhancing the clarity and insight of the plotted data.

Pending Tasks

• Further refinement of visualization techniques may be needed to address specific analytical goals or datasets.

Evidence

• source_file=2023-10-26.sessions.jsonl, line_number=1, event_count=0, session_id=081a413244eb0e497f13cbc9f23f0bf342f5f1590838c57026765980b1bd56fe
• event_ids: []