Enhanced Data Visualization with Rolling Average

• Day: 2024-09-28
• Time: 21:10 to 21:40
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Visualization, Python, Rolling Average, Efficiency, Predictive Modeling

Description

Session Goal

The goal of this session was to enhance [[data visualization]] techniques by incorporating a 1-year rolling average into existing plots, specifically focusing on the ‘valor’ variable.

Key Activities

• Analyzed time spent on various data tasks, emphasizing efficiency and time management improvements.
• Developed Python code to compute and plot a 1-year rolling average of the ‘valor’ variable using Pandas and Matplotlib.
• Discussed the implementation of a rolling average for poverty [[data visualization]], aimed at improving predictive modeling and automation.

Achievements

• Successfully integrated the rolling average into [[data visualization]] plots, providing a clearer trend analysis.
• Formulated recommendations for improving efficiency in future data tasks.

Pending Tasks

• Further refine the visualization techniques to incorporate additional variables and enhance clarity.
• Continue exploring automation opportunities in predictive modeling processes.

Evidence

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• event_ids: []