We start with the overview of the Python data visualization landscape and zero in on the core libraries for mapping. Day 1 covers vector data visualization with
#Maptplotlib,
#Pandas,
#GeoPandas, and
#Contextily (2/n)
Day 2 starts with a deep dive into XArray and raster data visualization. We use
#Xarray,
#rioxarray and
#CartoPy to visualize elevation and gridded climate datasets and learn some advanced
#Matplotlib tricks. (3/n)
On Day 3, we switch gears and start covering libraries for interactive mapping. We use
#Folium,
#GeoPandas, and
#Leafmap to create interactive maps with a range of geospatial datasets, including how to use Cloud-Optimized GeoTIFFs (
#COG) for visualizing large rasters. (4/n)
The last day is dedicated to learning how to build apps and dashboards with
#Streamlit. We put together everything we learned in the class so far - and build a dashboard, a geocoding app, and a multi-layer mapping app using
#Leafmap and publish it on
#Streamlit cloud (5/n)
The whole course is organized as a series of
#Jupyter notebooks that can be run on
#Colab with zero configuration. I hope this makes the exercises approachable to beginners. Check out the full course on our OpenCourseWare site and start learning!
courses.spatialthoughts.com/python-datavizβ¦This course is also offered as a live online cohort-based class that attracts professionals from across the world. The live classes come with free lifetime support and certification! Check out our instructor-led offerings at
spatialthoughts.com/events/