crs-l1-london-laea-areas

Rerun

import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Check the first few rows to understand the structure and CRS
    print("Columns:", gdf.columns.tolist())
    print("CRS:", gdf.crs)
    print(gdf.head())

    # Ensure we are working with a projected CRS to calculate area in square meters
    # If it's WGS84 (EPSG:4326), we need to reproject it.
    # A good choice for London is EPSG:27700 (British National Grid)
    if gdf.crs.is_geographic:
        gdf = gdf.to_crs(epsg=27700)
    
    # Calculate area in km2
    # gdf.area gives area in the units of the CRS (meters for EPSG:27700)
    gdf['area_km2'] = gdf.geometry.area / 10**6

    # Select required columns: id, name, and area_km2
    # The prompt says 'id' and 'name'. Let's see what the actual column names are from the print.
    # Often they are 'id' and 'name' or similar.
    # If 'id' is the index, we might need to reset it.
    
    # Let's assume the columns are named 'id' and 'name' based on the prompt.
    # If they are different, we will adjust.
    
    # Based on common GeoJSON structures, 'id' might be a property or the index.
    # We'll check for 'id' and 'name' in columns.
    
    # Preparing the output dataframe
    # We'll try to find the correct columns for id and name
    id_col = 'id' if 'id' in gdf.columns else gdf.index.name if gdf.index.name else 'index'
    name_col = 'name' if 'name' in gdf.columns else None
    
    # If 'id' is not a column but is the index, we'll handle that.
    # If 'name' is not found, we'll look for 'NAME' or something similar.
    if name_col is None:
        for col in ['NAME', 'name', 'Name', 'label']:
            if col in gdf.columns:
                name_col = col
                break
    
    if name_col is None:
        print("Warning: Could not find a name column. Using index.")
        name_col = gdf.index.name if gdf.index.name else 'index'

    # Create result dataframe
    # If 'id' is the index, we reset it to make it a column
    if id_col == 'index' or id_col == gdf.index.name:
        result = gdf.reset_index()
        # Rename the index column to 'id' if it's not already
        if 'index' in result.columns:
            result = result.rename(columns={'index': 'id'})
    else:
        result = gdf.copy()
        result['id'] = result[id_col]

    # Final selection
    output = result[['id', name_col, 'area_km2']].rename(columns={name_col: 'name'})
    
    # Save to CSV
    output.to_csv('borough_areas.csv', index=False)
    print("Successfully wrote borough_areas.csv")
    print(output.head())

if __name__ == "__main__":
    main()