This section provides introductory examples on the usage of Python and computational notebooks. These are high-level snapshots to give a basic indication of how these tools might typically be used; for more detailed examples, please refer to the online documentation for the libraries used.

If you would like to master python for spatial analysis, you are encouraged to avail yourself of online resources and roll up your proverbial sleeves: getting your hands dirty is the only reliable way to familiarise yourself with geospatial python and its capabilities.

1. Notebooks: Learn what computational notebooks are and how to use them.
2. Python Basics: Python basics in a nutshell, touching on variables, data types, and simple operations.
3. Spatial Data: Spatial data types and elementary operations using the shapely package.
4. GeoPandas: Use of the geopandas package for handling geospatial datasets.
5. OSM Data & Urban Morphology: Explore the wider Python urban analytics community by using the osmnx and momepy libraries to explore urban morphology.
6. Exploratory Data Science: Use the seaborn plotting library and scikit-learn for exploratory data science and predictive analytics.