spa-l1-paris-amenity-within

Rerun

import geopandas as gpd

def solve():
    # Load the layers
    amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')

    # Inspect column names to ensure we use the correct ones
    print("Amenities columns:", amenities.columns.tolist())
    print("Arrondissements columns:", arrondissements.columns.tolist())

    # Perform spatial join
    # amenities is points, arrondissements is polygons
    # We want to know which arrondissement each amenity is in
    joined = gpd.sjoin(amenities, arrondissements, predicate='within')

    # The prompt specifies the following columns:
    # osm_id, amenity_class, arrondissement_number (int), arrondissement_name (str)
    
    # We need to map the existing columns to these names.
    # Based on the print output (even if truncated), I'll need to check the actual names.
    # But let's assume:
    # amenities has 'osm_id' and some column for 'amenity_class' (maybe 'amenity'?)
    # arrondissements has 'arrondissement_number' and 'arrondissement_name' (or similar)
    
    # Let's look at the columns again via a more detailed print if needed, 
    # but let's try a robust approach.
    
    # First, let's find which column in 'amenities' is 'amenity_class'
    # Based on common OSM schemas, it's often 'amenity'.
    # Let's check the first few rows of amenities to be sure.
    
    # Since I can't "see" the print output of the previous command fully, 
    # I'll write a script that inspects and then performs the join.

    # Let's refine the script to actually do the mapping.
    pass

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')

    print("Amenities columns:", amenities.columns.tolist())
    print("Amenities head:\n", amenities.head())
    print("\nArrondissements columns:", arrondissements.columns.tolist())
    print("Arrondissements head:\n", arrondissements.head())

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    # Load layers
    amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')

    # Perform spatial join
    # Amenities (points) within arrondissements (polygons)
    joined = gpd.sjoin(amenities, arrondissements, predicate='within')

    # The requirement is:
    # osm_id, amenity_class, arrondissement_number (int), arrondissement_name (str)
    
    # Based on inspection:
    # amenities has: ['osm_id', 'amenity_class', 'name', 'geometry']
    # arrondissements has: ['id', 'name', 'geometry']
    
    # Wait, 'arrondissements' has 'id' and 'name'. 
    # 'id' is a UUID. I need 'arrondissement_number' and 'arrondissement_name'.
    # The prompt says: 'arrondissement_number' (integer), 'arrondissement_name' (string).
    # Let's check the 'arrondissements' layer again.
    # It seems I don't have 'arrondissement_number' in the 'arrondissements' layer 'id' or 'name' column.
    # Let me re-examine the 'arrondissements' head output.
    # The name of the arrondissement is likely in the 'name' column.
    # But where is the number?
    # In Paris, the name is usually "1er Arrondissement" or "1st Arrondissement" or just "1er".
    # Let's check the 'name' column values of arrondissements.

    print("Arrondissement names:", arrondissements['name'].tolist())

    # Let's check if 'name' contains the number.
    # If 'name' is like "1er Arrondissement", I can extract the number.
    
    # I will assume for now that I need to extract the number from 'name' if it's not there.
    # Or maybe there is another column I missed in the head view?
    # The head view showed: ['id', 'name', 'geometry']
    
    # Let's re-run with a more comprehensive inspection.
    pass

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')

    print("Amenities columns:", amenities.columns.tolist())
    print("Arrondissements columns:", arrondissements.columns.tolist())
    print("Arrondissements sample names:", arrondissements['name'].head(10).tolist())

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd
import re

def extract_number(name):
    # Matches "13e" or "13" or "13er" etc.
    match = re.search(r'(\d+)', name)
    if match:
        return int(match.group(1))
    return None

def solve():
    # Load layers
    amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')

    # Prepare arrondissements: extract number and use 'name' as arrondissement_name
    # arrondissement_name should be the string name as provided in the layer
    arrondissements['arrondissement_number'] = arrondissements['name'].apply(extract_number)
    arrondissements['arrondissement_name'] = arrondissements['name']

    # Perform spatial join
    # amenities (points) within arrondissements (polygons)
    # We want to keep amenity columns and the new arrondissement columns
    joined = gpd.sjoin(amenities, arrondissements[['arrondissement_number', 'arrondissement_name', 'geometry']], predicate='within')

    # Select and rename columns for final output
    # Desired: osm_id, amenity_class, arrondissement_number, arrondissement_name
    result = joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']]
    
    # Ensure types are correct
    result['arrondissement_number'] = result['arrondissement_number'].astype(int)
    result['arrondissement_name'] = result['arrondissement_name'].astype(str)
    result['osm_id'] = result['osm_id'].astype(int) # assuming it's an int or similar
    result['amenity_class'] = result['amenity_class'].astype(str)

    # Write to CSV
    result.to_csv('amenity_to_arrondissement.csv', index=False)
    print("Successfully wrote amenity_to_arrondissement.csv")
    print(result.head())

if __name__ == "__main__":
    solve()