Python Geospatial - Analysis Essentials [better]

Vectors (points/lines/polygons) are intuitive. Rasters are fast. Combined, they are unstoppable.

Geospatial data is everywhere. From tracking delivery trucks to analyzing climate change, location is the secret ingredient that makes data science actionable. Python GeoSpatial Analysis Essentials

# Extract row, col indices for each point rows, cols = src.index(points_proj.geometry.x, points_proj.geometry.y) Vectors (points/lines/polygons) are intuitive

districts = gpd.read_file("districts.geojson") with rasterio.open("elevation.tif") as src: # Loop over each district mean_elevations = [] for geom in districts.geometry: # Clip raster to polygon out_image, out_transform = mask(src, [geom], crop=True) # Calculate mean of valid pixels mean_elev = np.mean(out_image[out_image > -9999]) # Ignore NoData mean_elevations.append(mean_elev) col indices for each point rows