Python 101

This section introduces Python and computational notebooks for urban spatial analysis. These are high-level overviews of the key concepts and tools; for more depth, refer to the documentation for each library.

The best way to learn is by doing: experiment with the examples, break things, and consult the wider range of resources available online. Large Language Models can help accelerate your learning, but use them to deepen your understanding rather than as a substitute for it.

1. Notebooks: What computational notebooks are and how to use them.
2. Python Basics: Variables, data types, collections, control flow, and functions.
3. Spatial Data: Points, lines, and polygons with the shapely package.
4. GeoPandas: Handling geospatial datasets with geopandas.
5. Urban Analytics: Downloading OSM data with osmnx and analysing urban morphology with momepy.
6. Data Science: Dimensionality reduction, clustering, and prediction with seaborn and scikit-learn.