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"
}
0:03
Let me start by exploring the working directory to understand what data is available.
0:03
Bash
ls -la /work/
total 208 drwxr-xr-x 1 agent agent 40 Jun 17 00:35 . drwxr-xr-x 1 root root 14 Jun 17 00:35 .. -rw-r--r-- 1 agent agent 212992 Jun 17 00:35 paris_amenities.gpkg
0:06
Let me explore the data to understand the schema.
0:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# List layers in the gpkg
print(gpd.list_layers('paris_amenities.gpkg'))
"
name geometry_type 0 amenities Point 1 arrondissements Polygon
0:12
Great, there are two layers: `amenities` (Point) and `arrondissements` (Polygon). Let me inspect their schemas to understand the column names.
0:12
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(2))
print()
print('=== Arrondissements columns ===')
print(arr.columns.tolist())
print(arr.head(2))
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 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 rows x 3 columns]
CRS: EPSG:2154 EPSG:2154
0:17
Let me look at more data to understand the columns better. I need to check if there's an `arrondissement_number` in the arrondissements table. The `name` column likely has something like "Paris 1er", "Paris 2e", etc. I need to extract the number from the name. Let me check.
0:17
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
0:33
Great, I see the data. The arrondissement names contain the number (e.g., "Paris 13e Arrondissement", "Paris 1er Arrondissement"). I need to: 1. Extract the arrondissement number from the name 2. Perform a spatial join: amenities (points) inside arrondissements (polygons) 3. Output columns: osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string) Let me write a Python script to do this.
0:33
Write
/work/solve.py
content · 33 lines · py
import geopandas as gpd
import re
# Read layers
amen = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
arr = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")
# Extract arrondissement number from name (e.g. "Paris 13e Arrondissement" -> 13, "Paris 1er Arrondissement" -> 1)
def extract_number(name):
m = re.search(r'(\d+)', name)
return int(m.group(1)) if m else None
arr["arrondissement_number"] = arr["name"].apply(extract_number)
# Spatial join: point-in-polygon (inner join so only amenities inside an arrondissement are kept)
joined = gpd.sjoin(amen, arr, how="inner", predicate="within")
# Build output dataframe
result = joined[["osm_id", "amenity_class", "arrondissement_number", "name"]].copy()
result = result.rename(columns={"name": "arrondissement_name"})
result = result.drop_duplicates(subset=["osm_id"]) # avoid duplicates if on boundary
# Ensure arrondissement_number is integer type (nullable int)
result["arrondissement_number"] = result["arrondissement_number"].astype("Int64")
# Sort for reproducibility
result = result.sort_values(["osm_id"]).reset_index(drop=True)
# Write 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())Wrote /work/solve.py (1311 chars).
0:36
Let me run the script.
0:36
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 19, in <module>
result = joined[["osm_id", "amenity_class", "arrondissement_number", "name"]].copy()
~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1891, in __getitem__
result = super().__getitem__(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 4384, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6302, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6355, in _raise_if_missing
raise KeyError(f"{not_found} not in index")
KeyError: "['name'] not in index"
0:43
The issue is that after the spatial join, the columns from both dataframes are included but with suffixes. Let me check what columns are available.
0:43
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
amen = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arr = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
import re
def extract_number(name):
m = re.search(r'(\d+)', name)
return int(m.group(1)) if m else None
arr['arrondissement_number'] = arr['name'].apply(extract_number)
joined = gpd.sjoin(amen, arr, how='inner', predicate='within')
print(joined.columns.tolist())
print(joined.head(2))
"
['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id', 'name_right', 'arrondissement_number']
osm_id amenity_class ... name_right arrondissement_number
0 9000000000 restaurant ... Paris 10e Arrondissement 10
1 9000000001 restaurant ... Paris 4e Arrondissement 4
[2 rows x 8 columns]
0:50
I see - the column names have suffixes because both tables had a 'name' column. So 'name_left' is the amenity name, 'name_right' is the arrondissement name. Let me fix the script.
0:50
Edit
/work/solve.py
old_string not found in /work/solve.py
0:54
The edit failed because the file content might have changed slightly. Let me read it first.
0:54
Read
/work/solve.py
1 import geopandas as gpd
2 import re
3
4 # Read layers
5 amen = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
6 arr = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")
7
8 # Extract arrondissement number from name (e.g. "Paris 13e Arrondissement" -> 13, "Paris 1er Arrondissement" -> 1)
9 def extract_number(name):
10 m = re.search(r'(\d+)', name)
11 return int(m.group(1)) if m else None
12
13 arr["arrondissement_number"] = arr["name"].apply(extract_number)
14
15 # Spatial join: point-in-polygon (inner join so only amenities inside an arrondissement are kept)
16 joined = gpd.sjoin(amen, arr, how="inner", predicate="within")
17
18 # Build output dataframe
19 result = joined[["osm_id", "amenity_class", "arrondissement_number", "name"]].copy()
20 result = result.rename(columns={"name": "arrondissement_name"})
21 result = result.drop_duplicates(subset=["osm_id"]) # avoid duplicates if on boundary
22
23 # Ensure arrondissement_number is integer type (nullable int)
24 result["arrondissement_number"] = result["arrondissement_number"].astype("Int64")
25
26 # Sort for reproducibility
27 result = result.sort_values(["osm_id"]).reset_index(drop=True)
28
29 # Write CSV
30 result.to_csv("/work/amenity_to_arrondissement.csv", index=False)
31
32 print(f"Wrote {len(result)} rows to /work/amenity_to_arrondissement.csv")
33 print(result.head())
0:59
I see the issue. The column after sjoin is `name_right` not `name`. Let me fix it.
0:59
Edit
/work/solve.py
Edited /work/solve.py.
1:04
Now the column is `name_right` which was the arrondissement's name. Let me run again.
1:04
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
1:08
Let me verify the output file looks correct.
1:08
Bash
cd /work && head amenity_to_arrondissement.csv && echo "---" && wc -l amenity_to...
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 9000000004,restaurant,9,Paris 9e Arrondissement 9000000005,restaurant,15,Paris 15e Arrondissement 9000000006,restaurant,20,Paris 20e Arrondissement 9000000007,cafe,6,Paris 6e Arrondissement 9000000008,restaurant,18,Paris 18e Arrondissement --- 86 amenity_to_arrondissement.csv
1:17
The file has been created successfully. Let me verify the data looks correct.
1:17