📅 2024-09-28 — Session: Enhanced Data Visualization with Rolling Average
🕒 21:10–21:40
🏷️ Labels: Data Visualization, Python, Rolling Average, Efficiency, Predictive Modeling
📂 Project: Dev
⭐ Priority: MEDIUM
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.