Developed Geospatial and Temporal Data Exercises

• Day: 2025-03-10
• Time: 11:35 to 12:25
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Cleaning, Visualization, Pandas, Geopandas, Education

Description

Session Goal:

The session aimed to develop structured exercises for teaching data cleaning, transformation, and visualization techniques using Pandas and GeoPandas, focusing on geospatial and temporal datasets.

Key Activities:

• Created a structured statement for an exercise involving the cleaning and conversion of geospatial data to GeoDataFrame, and its visualization.
• Developed exercises for cleaning and visualizing geospatial data using GeoPandas and Folium, with a dataset of WiFi access points in Argentina.
• Designed exercises for manipulating and visualizing temporal data using Pandas, Matplotlib, and Seaborn, based on real datasets such as recipes.
• Updated and refined exercises on data manipulation, vectorized operations, and text manipulation in Pandas, including exercises on survey data analysis and transportation data.
• Created exercises for advanced Pandas operations using datasets like ‘diamonds’, focusing on grouping, aggregation, and transformation techniques.

Achievements:

• Successfully structured and outlined multiple exercises that cover a wide range of data analysis techniques using Python libraries such as Pandas, GeoPandas, Matplotlib, and Seaborn.
• Provided detailed guides and templates for students to learn and apply data cleaning, transformation, and visualization skills.

Pending Tasks:

• Review and test the developed exercises to ensure clarity and effectiveness for educational purposes.
• Gather feedback from students and educators to refine and improve the exercises further.

Evidence

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• event_ids: []