run-20260606-1733Z / spa-l1-paris-amenity-within

0:00

I'm putting together a neighbourhood demographic crosswalk for the INSEE census team and need each amenity tagged with the Paris arrondissement it falls inside. Can you join `amenities` to `arrondissements` and write the result to `amenity_to_arrondissement.csv` with one row per amenity? The columns are `osm_id`, `amenity_class`, `arrondissement_number` (integer), and `arrondissement_name` (string).

0:00

System info

{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}

0:02

Bash ls -F

paris_amenities.gpkg

0:03

Bash ogrinfo -so /work/paris_amenities.gpkg

INFO: Open of `/work/paris_amenities.gpkg'
      using driver `GPKG' successful.
1: amenities (Point)
2: arrondissements (Polygon)

0:07

Bash ogrinfo -al -so /work/paris_amenities.gpkg amenities

INFO: Open of `/work/paris_amenities.gpkg'
      using driver `GPKG' successful.

Layer name: amenities
Geometry: Point
Feature Count: 85
Extent: (645944.178293, 6857854.038066) - (660329.761503, 6866790.507853)
Layer SRS WKT:
PROJCRS["RGF93 v1 / Lambert-93",
    BASEGEOGCRS["RGF93 v1",
        DATUM["Reseau Geodesique Francais 1993 v1",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4171]],
    CONVERSION["Lambert-93",
        METHOD["Lambert Conic Conformal (2SP)",
            ID["EPSG",9802]],
        PARAMETER["Latitude of false origin",46.5,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8821]],
        PARAMETER["Longitude of false origin",3,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8822]],
        PARAMETER["Latitude of 1st standard parallel",49,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8823]],
        PARAMETER["Latitude of 2nd standard parallel",44,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8824]],
        PARAMETER["Easting at false origin",700000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8826]],
        PARAMETER["Northing at false origin",6600000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8827]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["France - onshore and offshore, mainland and Corsica (France métropolitaine including Corsica)."],
        BBOX[41.15,-9.86,51.56,10.38]],
    ID["EPSG",2154]]
Data axis to CRS axis mapping: 1,2
FID Column = fid
Geometry Column = geom
osm_id: Integer64 (0.0)
amenity_class: String (0.0)
name: String (0.0)

0:09

Bash ogrinfo -al -so /work/paris_amenities.gpkg arrondissements

INFO: Open of `/work/paris_amenities.gpkg'
      using driver `GPKG' successful.

Layer name: arrondissements
Geometry: Polygon
Feature Count: 20
Extent: (643069.079698, 6857478.445150) - (661079.519663, 6867081.892415)
Layer SRS WKT:
PROJCRS["RGF93 v1 / Lambert-93",
    BASEGEOGCRS["RGF93 v1",
        DATUM["Reseau Geodesique Francais 1993 v1",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4171]],
    CONVERSION["Lambert-93",
        METHOD["Lambert Conic Conformal (2SP)",
            ID["EPSG",9802]],
        PARAMETER["Latitude of false origin",46.5,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8821]],
        PARAMETER["Longitude of false origin",3,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8822]],
        PARAMETER["Latitude of 1st standard parallel",49,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8823]],
        PARAMETER["Latitude of 2nd standard parallel",44,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8824]],
        PARAMETER["Easting at false origin",700000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8826]],
        PARAMETER["Northing at false origin",6600000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8827]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["France - onshore and offshore, mainland and Corsica (France métropolitaine including Corsica)."],
        BBOX[41.15,-9.86,51.56,10.38]],
    ID["EPSG",2154]]
Data axis to CRS axis mapping: 1,2
FID Column = fid
Geometry Column = geom
id: String (0.0)
name: String (0.0)

0:26

Write /work/solve.py

content · 70 lines · py

# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pyproj", "pandas"]
# ///

import geopandas as gpd
import pandas as pd

def main():
    # Load the data
    input_file = "/work/paris_amenities.gpkg"
    amenities = gpd.read_file(input_file, layer='amenities')
    arrondissements = gpd.read_file(input_file, layer='arrondissements')

    # Check columns in arrondissements to identify number and name
    # Based on ogrinfo: id (String), name (String)
    # Wait, the user wants 'arrondissement_number' (integer) and 'arrondissement_name' (string)
    # Let's look at the 'id' column in arrondissements. If it's "1", "2", etc.
    # If it's "1er Arrondissement", we might need to parse it.
    
    # Perform spatial join
    # amenities (points) join with arrondissements (polygons)
    joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="within")

    # The requested columns are: osm_id, amenity_class, arrondissement_number, arrondissement_name
    # amenities columns: osm_id, amenity_class, name, geom
    # arrondissements columns: id, name, geom
    
    # After sjoin, columns from arrondissements are prefixed or kept. 
    # Let's inspect columns first.
    print("Joined columns:", joined.columns.tolist())

    # Mapping columns
    # We'll assume 'id' from arrondissements is the number and 'name' is the name.
    # However, 'id' might be something like '1' or 'arrondissement_1'.
    # Let's try to convert 'id' to integer.
    
    # Create the final dataframe
    result = pd.DataFrame()
    result['osm_id'] = joined['osm_id']
    result['amenity_class'] = joined['amenity_class']
    
    # arrondissement_number (integer)
    # If 'id' is not easily convertible, we might need to extract digits.
    # Let's try a simple conversion first.
    try:
        result['arrondissement_number'] = joined['id'].astype(int)
    except Exception as e:
        print(f"Error converting id to int: {e}")
        # Fallback: extract digits
        result['arrondissement_number'] = joined['id'].str.extract(r'(\d+)').astype(float).astype(int)

    # arrondissement_name (string)
    result['arrondissement_name'] = joined['name'].astype(str)

    # Drop rows where no arrondissement was found (if any)
    # The prompt implies each amenity should be tagged.
    # If there are NaNs in arrondissement_number after conversion, they might be outside.
    # But for a crosswalk, we usually want all amenities that fell in one.
    # Let's keep only those that joined successfully.
    result = result.dropna(subset=['arrondissement_number'])

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

if __name__ == "__main__":
    main()

Wrote /work/solve.py (2827 chars).

0:27

Bash uv run /work/solve.py

Joined columns: ['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id', 'name_right']
Error converting id to int: invalid literal for int() with base 10: '39c0e434-e46e-4ac9-b35a-100f804ede99'

Installed 10 packages in 248ms
Traceback (most recent call last):
  File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3641, in get_loc
    return self._engine.get_loc(casted_key)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
  File "pandas/_libs/index.pyx", line 168, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 197, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 7668, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 7676, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'name'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/work/solve.py", line 69, in <module>
    main()
    ~~~~^^
  File "/work/solve.py", line 54, in main
    result['arrondissement_name'] = joined['name'].astype(str)
                                    ~~~~~~^^^^^^^^
  File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1891, in __getitem__
    result = super().__getitem__(key)
  File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/frame.py", line 4378, in __getitem__
    indexer = self.columns.get_loc(key)
  File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3648, in get_loc
    raise KeyError(key) from err
KeyError: 'name'

0:55

Write /work/solve.py

content · 65 lines · py

# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pyproj", "pandas"]
# ///

import geopandas as gpd
import pandas as pd
import re

def main():
    # Load the data
    input_file = "/work/paris_amenities.gpkg"
    amenities = gpd.read_file(input_file, layer='amenities')
    arrondissements = gpd.read_file(input_file, layer='arrondissements')

    # Perform spatial join
    # amenities (points) join with arrondissements (polygons)
    joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="within")

    # Based on error log, columns after sjoin are:
    # ['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id', 'name_right']
    # 'id' is a UUID-like string, not the number.
    # 'name_right' is the name of the arrondissement.
    # Wait, how do we get the number if 'id' is a UUID?
    # Let's check the name_right content. It might contain the number.
    
    print("Sample joined rows:")
    print(joined[['id', 'name_right']].head())

    # We need arrondissement_number (int) and arrondissement_name (str).
    # Let's inspect names to see if they follow a pattern like "1er Arrondissement" or "1er" or "Arrondissement 1".
    
    # Create the final dataframe
    result = pd.DataFrame()
    result['osm_id'] = joined['osm_id']
    result['amenity_class'] = joined['amenity_class']
    
    # The 'name_right' column should contain the arrondissement name.
    result['arrondissement_name'] = joined['name_right'].astype(str)
    
    # Extract number from name_right
    def extract_number(name):
        if pd.isna(name) or name == 'nan':
            return None
        # Look for digits in the string
        match = re.search(r'(\d+)', str(name))
        if match:
            return int(match.group(1))
        return None

    result['arrondissement_number'] = result['arrondissement_name'].apply(extract_number)

    # Drop rows where no arrondissement was found
    result = result.dropna(subset=['arrondissement_number'])
    result['arrondissement_number'] = result['arrondissement_number'].astype(int)

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

if __name__ == "__main__":
    main()

Wrote /work/solve.py (2348 chars).

0:58

Bash uv run /work/solve.py

Sample joined rows:
                                     id                name_right
0  39c0e434-e46e-4ac9-b35a-100f804ede99  Paris 10e Arrondissement
1  98f4ac3a-fc4a-40de-b535-2ebfa6f4e202   Paris 4e Arrondissement
2  39c0e434-e46e-4ac9-b35a-100f804ede99  Paris 10e Arrondissement
3  0cef9694-93f0-4acf-a44f-f54a01fce855  Paris 13e Arrondissement
4  75e0a62a-117b-46e6-8782-a4b225ce37e4   Paris 9e Arrondissement
Successfully wrote 85 rows to /work/amenity_to_arrondissement.csv
       osm_id amenity_class       arrondissement_name  arrondissement_number
0  9000000000    restaurant  Paris 10e Arrondissement                     10
1  9000000001    restaurant   Paris 4e Arrondissement                      4
2  9000000002        bakery  Paris 10e Arrondissement                     10
3  9000000003        bakery  Paris 13e Arrondissement                     13
4  9000000004    restaurant   Paris 9e Arrondissement                      9

1:02

Bash head -n 5 /work/amenity_to_arrondissement.csv

osm_id,amenity_class,arrondissement_name,arrondissement_number
9000000000,restaurant,Paris 10e Arrondissement,10
9000000001,restaurant,Paris 4e Arrondissement,4
9000000002,bakery,Paris 10e Arrondissement,10
9000000003,bakery,Paris 13e Arrondissement,13

1:05

Bash head -n 1 /work/amenity_to_arrondissement.csv

osm_id,amenity_class,arrondissement_name,arrondissement_number

1:22

Write /work/solve.py

content · 56 lines · py

# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pyproj", "pandas"]
# ///

import geopandas as gpd
import pandas as pd
import re

def main():
    # Load the data
    input_file = "/work/paris_amenities.gpkg"
    amenities = gpd.read_file(input_file, layer='amenities')
    arrondissements = gpd.read_file(input_file, layer='arrondissements')

    # Perform spatial join
    # amenities (points) join with arrondissements (polygons)
    joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="within")

    # Create the final dataframe with specific column order requested:
    # osm_id, amenity_class, arrondissement_number (integer), and arrondissement_name (string).
    result = pd.DataFrame()
    result['osm_id'] = joined['osm_id']
    result['amenity_class'] = joined['amenity_class']
    
    # Use 'name_right' as the arrondissement_name source
    result['arrondissement_name'] = joined['name_right'].astype(str)
    
    # Extract number from name_right (e.g., "Paris 10e Arrondissement" -> 10)
    def extract_number(name):
        if pd.isna(name) or name == 'nan':
            return None
        # Look for digits in the string
        match = re.search(r'(\d+)', str(name))
        if match:
            return int(match.group(1))
        return None

    result['arrondissement_number'] = result['arrondissement_name'].apply(extract_number)

    # Drop rows where no arrondissement was found
    result = result.dropna(subset=['arrondissement_number'])
    result['arrondissement_number'] = result['arrondissement_number'].astype(int)

    # Reorder columns to match requirement exactly:
    # osm_id, amenity_class, arrondissement_number, arrondissement_name
    result = result[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']]

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

if __name__ == "__main__":
    main()

Wrote /work/solve.py (2051 chars).

1:24

Bash uv run /work/solve.py && head -n 5 /work/amenity_to_arrondissement.csv

Successfully wrote 85 rows to /work/amenity_to_arrondissement.csv
osm_id,amenity_class,arrondissement_number,arrondissement_name
9000000000,restaurant,10,Paris 10e Arrondissement
9000000001,restaurant,4,Paris 4e Arrondissement
9000000002,bakery,10,Paris 10e Arrondissement
9000000003,bakery,13,Paris 13e Arrondissement

1:40

spa-l1-paris-amenity-within

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spa-l1-paris-amenity-within