crs-l1-london-laea-areas

Rerun

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Ensure we are using a projected CRS to calculate area in meters
    # London is typically around EPSG:27700 (British National Grid)
    # We should check the current CRS first
    print(f"Original CRS: {gdf.crs}")
    
    # Project to a suitable CRS for area calculation if not already projected
    # EPSG:27700 is standard for UK/London
    if gdf.crs is None or gdf.crs.is_geographic:
        gdf_projected = gdf.to_crs(epsg=27700)
    else:
        gdf_projected = gdf
        
    # Calculate area in square meters, then convert to km^2
    gdf_projected['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # Prepare the output dataframe
    # The user wants id, name, and area_km2
    # We need to identify which columns correspond to id and name.
    # Let's inspect the columns first.
    print("Columns in gdf:", gdf.columns.tolist())
    
    # Assuming 'id' and 'name' (or similar) are the column names.
    # If 'id' is the index or a specific column, we'll use it.
    # Based on prompt: "containing its id, name, and area_km2"
    
    # Let's check the first few rows to confirm column names
    print(gdf.head())

    # Create the output dataframe
    # We'll try to find the id and name columns. 
    # Often they might be 'id' and 'name', or 'id' and 'name_en' etc.
    # If 'id' is the index, we reset it.
    
    output_gdf = gdf.copy()
    output_gdf['area_km2'] = gdf_projected['area_km2']
    
    # Select only the required columns. 
    # Note: The prompt says "its id, name, and area_km2". 
    # If 'id' is not a column, we might need to use the index.
    
    # Let's assume 'id' and 'name' are the column names.
    # If they are not, we will adjust.
    
    result = output_gdf[['id', 'name', 'area_km2']]
    
    # Save to csv
    result.to_csv('borough_areas.csv', index=False)
    print("Successfully saved borough_areas.csv")

if __name__ == "__main__":
    main()