spa-l1-paris-amenity-within
Map (reference ← swipe → agent)
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": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:13
Let me start by exploring the working directory to understand what data files are available.
0:13
Bash
ls -la /work/
total 208 drwxr-xr-x 1 agent agent 40 Jun 18 07:14 . drwxr-xr-x 1 root root 14 Jun 18 07:14 .. -rw-r--r-- 1 agent agent 212992 Jun 18 07:14 paris_amenities.gpkg
0:19
Only one file. Let me check what layers are in this GeoPackage.
0:19
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
layers = gpd.list_layers('paris_amenities.gpkg')
print(layers)
"
name geometry_type 0 amenities Point 1 arrondissements Polygon
0:27
Let's look at the columns of both layers.
0:27
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
amen = gpd.read_file('paris_amenities.gpkg', layer='amenities')
arr = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
print('=== amenities columns ===')
print(amen.columns.tolist())
print(amen.head(3))
print()
print('=== arrondissements columns ===')
print(arr.columns.tolist())
print(arr.head(3))
print()
print('CRS:', amen.crs, arr.crs)
"
=== amenities columns ===
['osm_id', 'amenity_class', 'name', 'geometry']
osm_id ... geometry
0 9000000000 ... POINT (652773.528 6864033.347)
1 9000000001 ... POINT (652322.91 6861774.142)
2 9000000002 ... POINT (653028.103 6863582.051)
[3 rows x 4 columns]
=== arrondissements columns ===
['id', 'name', 'geometry']
id ... geometry
0 0cef9694-93f0-4acf-a44f-f54a01fce855 ... POLYGON ((651737.858 6858104.126, 651824.872 6...
1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 ... POLYGON ((651390.128 6862244.403, 651327.017 6...
2 2747f137-3f7b-4ac4-b222-b4ac7acef256 ... POLYGON ((653870.967 6863517.544, 653863.816 6...
[3 rows x 3 columns]
CRS: EPSG:2154 EPSG:2154
0:31
Let me understand the data: 1. **amenities** has: `osm_id`, `amenity_class`, `name`, `geometry` (Point) 2. **arrondissements** has: `id`, `name`, `geometry` (Polygon) The task asks for columns: - `osm_id` (from amenities) - `amenity_class` (from amenities) - `arrondissement_number` (integer) - I need to figure out what this is. The arrondissements have an `id` (UUID) and a `name`. The name might be like "Paris 1er Arrondissement" or something. Let me look at the names. - `arrondissement_name` (string) Let me look at the arrondissements data more closely.
0:31
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
arr = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
print(arr[['id','name']])
"
id name 0 0cef9694-93f0-4acf-a44f-f54a01fce855 Paris 13e Arrondissement 1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 Paris 6e Arrondissement 2 2747f137-3f7b-4ac4-b222-b4ac7acef256 Paris 11e Arrondissement 3 32fbdc6a-f89d-4d02-ac87-6d0e4fc98969 Paris 3e Arrondissement 4 366fc115-880d-47a0-90d8-95f28f275224 Paris 2e Arrondissement 5 39c0e434-e46e-4ac9-b35a-100f804ede99 Paris 10e Arrondissement 6 4687f7be-db17-41c3-a9d0-09afa5523060 Paris 20e Arrondissement 7 4e7c04fe-3c0d-4401-b79d-ce3e2395e5fd Paris 8e Arrondissement 8 5134a455-e91f-4fdb-8638-c33ee7cb1383 Paris 19e Arrondissement 9 5537e76e-52db-4342-938d-a91168f4e6a7 Paris 5e Arrondissement 10 571cb253-40c3-4761-b663-d30deedf713f Paris 17e Arrondissement 11 75e0a62a-117b-46e6-8782-a4b225ce37e4 Paris 9e Arrondissement 12 7caa32e9-d051-48f2-87ae-29d09104547e Paris 16e Arrondissement 13 818e7bcb-32e9-4567-919a-c44153254398 Paris 14e Arrondissement 14 8a6c934a-8534-4e30-a9e5-e827de7a9891 Paris 12e Arrondissement 15 98f4ac3a-fc4a-40de-b535-2ebfa6f4e202 Paris 4e Arrondissement 16 dcc8aaa3-2adb-424f-a745-b9a23163f7c0 Paris 18e Arrondissement 17 ea79b3d9-590d-450e-8da6-30cb1b6fcc66 Paris 7e Arrondissement 18 ed5166f4-7111-4e42-bcbe-0630271cc71e Paris 1er Arrondissement 19 f22f8b89-a6ec-449f-bc9a-cf3c7644d0e0 Paris 15e Arrondissement
0:38
Now I need to extract the arrondissement number from the name. The names look like: - "Paris 1er Arrondissement" - "Paris 2e Arrondissement" - etc. So I need to parse the number. Let me write a spatial join script.
0:38
Write
/work/solve.py
content · 36 lines · py
import geopandas as gpd
import re
# Load data
amen = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arr = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
# Ensure same CRS
assert amen.crs == arr.crs, "CRS mismatch"
# Spatial join: each amenity gets the arrondissement it falls inside
joined = gpd.sjoin(amen, arr, how='left', predicate='within')
# Parse arrondissement number from name
def extract_number(name):
# Names like "Paris 1er Arrondissement", "Paris 2e Arrondissement", ...
m = re.search(r'(\d+)', name)
if m:
return int(m.group(1))
return None
joined['arrondissement_number'] = joined['name_right'].apply(extract_number)
joined['arrondissement_name'] = joined['name_right']
# Select and rename columns as required
result = joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()
# Sort for reproducibility
result = result.sort_values('osm_id').reset_index(drop=True)
# Write to CSV
result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
print(f"Wrote {len(result)} rows to /work/amenity_to_arrondissement.csv")
print(result.head())
print(result.dtypes)Wrote /work/solve.py (1179 chars).
0:41
Let me run it.
0:41
Bash
cd /work && python solve.py
Wrote 85 rows to /work/amenity_to_arrondissement.csv
osm_id amenity_class arrondissement_number arrondissement_name
0 9000000000 restaurant 10 Paris 10e Arrondissement
1 9000000001 restaurant 4 Paris 4e Arrondissement
2 9000000002 bakery 10 Paris 10e Arrondissement
3 9000000003 bakery 13 Paris 13e Arrondissement
4 9000000004 restaurant 9 Paris 9e Arrondissement
osm_id int64
amenity_class str
arrondissement_number int64
arrondissement_name str
dtype: object
0:45
The script ran successfully. Let me verify the output file to make sure it looks correct.
0:45
Read
/work/amenity_to_arrondissement.csv
1 osm_id,amenity_class,arrondissement_number,arrondissement_name
2 9000000000,restaurant,10,Paris 10e Arrondissement
3 9000000001,restaurant,4,Paris 4e Arrondissement
4 9000000002,bakery,10,Paris 10e Arrondissement
5 9000000003,bakery,13,Paris 13e Arrondissement
6 9000000004,restaurant,9,Paris 9e Arrondissement
7 9000000005,restaurant,15,Paris 15e Arrondissement
8 9000000006,restaurant,20,Paris 20e Arrondissement
9 9000000007,cafe,6,Paris 6e Arrondissement
10 9000000008,restaurant,18,Paris 18e Arrondissement
11 9000000009,cafe,5,Paris 5e Arrondissement
12 9000000010,restaurant,17,Paris 17e Arrondissement
13 9000000011,restaurant,6,Paris 6e Arrondissement
14 9000000012,restaurant,8,Paris 8e Arrondissement
15 9000000013,restaurant,1,Paris 1er Arrondissement
16 9000000014,restaurant,10,Paris 10e Arrondissement
17 9000000015,cafe,15,Paris 15e Arrondissement
18 9000000016,cafe,12,Paris 12e Arrondissement
19 9000000017,bakery,15,Paris 15e Arrondissement
20 9000000018,cafe,3,Paris 3e Arrondissement
<system-reminder>Showed lines 1-20 of 86. Re-call Read with offset=21 to continue, or raise limit.</system-reminder>
0:49
The output looks good. Let me verify there are no NaN values (amenities that didn't fall inside any arrondissement).
0:49
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_csv('/work/amenity_to_arrondissement.csv')
print('Total rows:', len(df))
print('Missing arrondissement_number:', df['arrondissement_number'].isna().sum())
print('Missing arrondissement_name:', df['arrondissement_name'].isna().sum())
print()
print('All rows accounted for with an arrondissement:', df['arrondissement_number'].notna().all())
"
Total rows: 85 Missing arrondissement_number: 0 Missing arrondissement_name: 0 All rows accounted for with an arrondissement: True
1:06
The task is complete. I loaded the two layers from the GeoPackage, performed a spatial join (point-in-polygon) to assign each amenity to the arrondissement it falls inside, extracted the arrondissement number from the name field, and wrote the result as a CSV with the requested columns. All 85 amenities were successfully matched to an arrondissement.
1:06