To master this field, one must navigate a rich and rapidly evolving stack of libraries. Unlike the general data science stack (NumPy/Pandas), the geospatial stack is specialized for handling coordinate reference systems (CRS), vector geometries, and raster matrices.
Here is an example of how to use Geopandas and Folium to load and visualize geospatial data: applied geospatial data science with python pdf
# Add the data to the map folium.GeoJson(gdf.__geo_interface__).add_to(m) To master this field, one must navigate a
import geopandas as gpd import folium
# Create a Folium map m = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], zoom_start=10) To master this field