spa-l1-paris-amenity-within

Rerun

import geopandas as gpd

def solve():
    # Load the data
    gdf = gpd.read_file('/work/paris_amenities.gpkg')
    
    # Inspect layer names and columns
    print("Layers in GPKG:")
    print(gdf.layers)
    
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    print("\nAmenities columns:")
    print(amenities.columns)
    print(amenities.head())
    
    print("\nArrondissements columns:")
    print(arrondissements.columns)
    print(arrondissements.head())

    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Spatial join
    # We want to find which arrondissement each amenity point falls into
    joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')

    # The user requested specific columns:
    # osm_id, amenity_class, arrondissement_number (int), arrondissement_name (str)
    
    # Let's check what the columns are actually named in the files.
    # Based on the prompt, I'll map them.
    # I'll assume the column names might be slightly different and I'll try to find them.
    
    # Check column mapping
    # We'll need to identify which columns correspond to osm_id, amenity_class, arrondissement_number, and arrondissement_name.
    
    # For osm_id, let's look for 'id' or 'osm_id'
    # For amenity_class, let's look for 'amenity' or 'class'
    # For arrondissement_number, let's look for 'number' or 'num'
    # For arrondissement_name, let's look for 'name' or 'arrondissement'
    
    # However, I'll first print them to be sure.
    
    # Let's assume standard naming for now and verify in the next step if it fails.
    # But let's look at the column names printed above.
    
    # Since I haven't run the script yet, I'll just write the logic and run it.
    
    # Rename columns based on what we find.
    # Mapping logic:
    mapping = {
        'osm_id': None,
        'amenity_class': None,
        'arrondissement_number': None,
        'arrondissement_name': None
    }
    
    # This is a bit of a guessing game without seeing the columns first, 
    # so I will print them first.
    
    pass

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    print("Amenities columns:", amenities.columns.tolist())
    print("Arrondissements columns:", arrondissements.columns.tolist())

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd

def solve():
    # Load the data
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Spatial join
    # We want to find which arrondissement each amenity point falls into
    joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')

    # The user requested specific columns:
    # osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string)
    
    # From previous output:
    # amenities: ['osm_id', 'amenity_class', 'name', 'geometry']
    # arrondissements: ['id', 'name', 'geometry']
    
    # Wait, 'arrondissements' columns were ['id', 'name', 'geometry'].
    # The prompt asks for 'arrondissement_number' (integer) and 'arrondissement_name' (string).
    # I should check if 'id' is the number or if I need to extract it.
    # Let's check the content of 'id' in arrondissements.
    
    # I'll perform a quick check on the arrondissement 'id' column.
    print("Arrondissements sample:")
    print(arrondissements[['id', 'name']].head())

    # Let's refine the mapping:
    # osm_id -> osm_id
    # amenity_class -> amenity_class
    # arrondissement_number -> id (must be int)
    # arrondissement_name -> name (string)
    
    # Note: In spatial joins, if 'name' exists in both, it might become 'name_left' and 'name_right'
    # or if using sjoin, it might rename them. 
    # In geopandas sjoin, columns with same names in both GDFs get suffix.
    # However, 'amenities' has 'name' and 'arrondissements' has 'name'.
    # So 'amenities.name' becomes 'name_left' and 'arrondissements.name' becomes 'name_right'.
    
    # Let's re-run with a more robust column selection after joining.
    
    # If 'id' is the number, I'll use it.
    # If 'name' is the name, I'll use it.
    
    # Let's check the data types and values of 'id' first.
    pass

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd

def solve():
    # Load the data
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Rename arrondissement columns before joining to avoid conflicts and make selection easy
    arrondissements_renamed = arrondissements.rename(columns={
        'id': 'arrondissement_number',
        'name': 'arrondissement_name'
    })
    
    # Spatial join: find which arrondissement each amenity point falls into
    joined = gpd.sjoin(amenities, arrondissements_renamed, how='left', predicate='within')

    # Select only requested columns: osm_id, amenity_class, arrondissement_number, arrondissement_name
    # We use a list to ensure we don't get 'geometry' or 'index_right'
    cols_to_keep = ['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']
    
    # Filter to ensure we only keep columns that actually exist in the joined dataframe
    existing_cols = [c for c in cols_to_keep if c in joined.columns]
    result = joined[existing_cols].copy()
    
    # Drop amenities that didn't fall into an arrondissement to ensure integer type for number
    result = result.dropna(subset=['arrondissement_number'])
    
    # Convert types as requested
    result['arrondissement_number'] = result['arrondissement_number'].astype(int)
    result['arrondissement_name'] = result['arrondissement_name'].astype(str)
    
    # Final column order check/reorder
    result = result[cols_to_keep]

    # Save to CSV
    result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
    print(f"Successfully wrote {len(result)} rows to /work/amenity_to_arrondissement.csv")

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd

def solve():
    # Load the data
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Rename arrondissement columns before joining to avoid conflicts and make selection easy
    arrondissements_renamed = arrondissements.rename(columns={
        'id': 'arrondissement_number',
        'name': 'arrondissement_name'
    })
    
    # Spatial join: find which arrondissement each amenity point falls into
    joined = gpd.sjoin(amenities, arrondissements_renamed, how='left', predicate='within')

    # The user wants: osm_id, amenity_class, arrondissement_number (int), arrondissement_name (str)
    # Looking at the error: 'id' in arrondissements is NOT the number, but a UUID.
    # Let's check the columns again and see if there's a better column for the number.
    
    # I'll print the first few rows of arrondissements to find the correct number column.
    print("Arrondissements Head:")
    print(arrondissements.head())
    
    # Wait, I cannot see the output of the previous run easily in my head, 
    # but the error message tells me 'id' is a UUID: '39c0e434-e46e-4ac9-b35a-100f804ede99'
    # I need to find the column that contains the number (1, 2, 3...).
    
    pass

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd

def solve():
    # Load the data
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    print("Amenities columns:", amenities.columns.tolist())
    print("Arrondissements columns:", arrondissements.columns.tolist())
    print("\nArrondissements sample data:")
    print(arrondissements.head())

    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Spatial join
    joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')
    print("\nJoined columns:", joined.columns.tolist())

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    print("Full columns in arrondissements:", arrondissements.columns.tolist())

if __name__ == "__main__":
    solve()

import geopandas as gpd

def solve():
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    print("Arrondissement names sample:")
    print(arrondissements['name'].head())

if __name__ == "__main__":
    solve()

import geopandas as gpd
import pandas as pd
import re

def extract_number(name):
    # Looking for "13e", "6e", "11e" etc.
    # We want the digits before the 'e'
    match = re.search(r'(\d+)e', name)
    if match:
        return int(match.group(1))
    return None

def solve():
    # Load the data
    amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
    arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
    
    # Ensure CRS is the same
    if amenities.crs != arrondissements.crs:
        amenities = amenities.to_crs(arrondissements.crs)

    # Add arrondissement_number to arrondissements
    arrondissements['arrondissement_number'] = arrondissements['name'].apply(extract_number)
    
    # Rename for clarity before join
    arrondissements_renamed = arrondissements.rename(columns={
        'name': 'arrondissement_name'
    })
    
    # Spatial join
    joined = gpd.sjoin(amenities, arrondissements_renamed, how='left', predicate='within')

    # Select requested columns: osm_id, amenity_class, arrondissement_number, arrondissement_name
    cols_to_keep = ['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']
    
    # Filter to only include amenities that actually fell into an arrondissement 
    # to ensure we can cast to int and fulfill the requirement "tagged with the Paris arrondissement"
    result = joined.dropna(subset=['arrondissement_number']).copy()
    
    # Convert types
    result['arrondissement_number'] = result['arrondissement_number'].astype(int)
    result['arrondissement_name'] = result['arrondissement_name'].astype(str)
    
    # Final column order
    result = result[cols_to_keep]

    # Save to CSV
    result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
    print(f"Successfully wrote {len(result)} rows to /work/amenity_to_arrondissement.csv")

if __name__ == "__main__":
    solve()