📅 2025-03-10 — Session: Developed Geospatial and Temporal Data Exercises
🕒 11:35–12:25
🏷️ Labels: Data Cleaning, Visualization, Pandas, Geopandas, Education
📂 Project: Teaching
⭐ Priority: MEDIUM
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.