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 reading the working directory to see what files are available.
0:03
Bash
ls -la /work/
total 208 drwxr-xr-x 1 agent agent 40 Jun 18 01:47 . drwxr-xr-x 1 root root 14 Jun 18 01:47 .. -rw-r--r-- 1 agent agent 212992 Jun 18 01:47 paris_amenities.gpkg
0:06
Let's see what data we have. Only one file mentioned - `paris_amenities.gpkg`. But the task mentions both `amenities` and `arrondissements`. Let me explore this geopackage first.
0:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gpd.read_file('paris_amenities.gpkg', layer=None)
"
/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py:382: UserWarning: More than one layer found in 'paris_amenities.gpkg': 'amenities' (default), 'arrondissements'. Specify layer parameter to avoid this warning. result = read_func(
0:11
Good, so the GPKG has two layers: "amenities" and "arrondissements". Let me inspect both.
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Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
amen = gpd.read_file('paris_amenities.gpkg', layer='amenities')
print('=== amenities ===')
print(amen.columns.tolist())
print(amen.head(5))
print(amen.shape)
print(amen.crs)
"
=== amenities ===
['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 9000000003 ... POINT (653087.577 6858190.655)
4 9000000004 ... POINT (650836.831 6863603.61)
[5 rows x 4 columns]
(85, 4)
EPSG:2154
0:15
The CRS is EPSG:2154 (Lambert 93, the French projected CRS). Let's look at arrondissements.
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Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
arr = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
print('=== arrondissements ===')
print(arr.columns.tolist())
print(arr.head(5))
print(arr.shape)
print(arr.crs)
"
=== arrondissements ===
['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 32fbdc6a-f89d-4d02-ac87-6d0e4fc98969 ... POLYGON ((653556.757 6862647.528, 653554.105 6...
4 366fc115-880d-47a0-90d8-95f28f275224 ... POLYGON ((651998.253 6863647.264, 651855.796 6...
[5 rows x 3 columns]
(20, 3)
EPSG:2154
0:20
Let me check the data more carefully. The arrondissements have an `id` and `name` field. The user wants `arrondissement_number` (integer) and `arrondissement_name` (string). The name column probably has something like "Paris 1er Arrondissement", "Paris 2e Arrondissement", etc. I need to extract the number from the name or from the id. Let me look at the arrondissement data more closely.
0:20
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
0:28
Now I understand the data: - `amenities` layer has: `osm_id`, `amenity_class`, `name`, `geometry` (85 point features, EPSG:2154) - `arrondissements` layer has: `id`, `name`, `geometry` (20 polygon features, EPSG:2154) The arrondissement name format is like "Paris 13e Arrondissement", "Paris 1er Arrondissement". I need to extract the arrondissement number (integer) from the name, and keep the name as-is. The name format is: - "Paris 1er Arrondissement" → number 1, name "Paris 1er Arrondissement" - "Paris 2e Arrondissement" → number 2, name "Paris 2e Arrondissement" - etc. So I need to parse "1er" as 1, "2e" as 2, etc. I'll write a spatial join and extract the arrondissement number. Let me write the Python script.
0:28
Write
/work/solve.py
content · 51 lines · py
import geopandas as gpd
import re
# Read both layers
amenities = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
arrondissements = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")
# Ensure same CRS (they both already are EPSG:2154)
print(f"Amenities CRS: {amenities.crs}")
print(f"Arrondissements CRS: {arrondissements.crs}")
# Spatial join: for each amenity (point), find the containing arrondissement polygon
# Using predicate='within' for points-in-polygons
joined = gpd.sjoin(
amenities,
arrondissements,
how="left",
predicate="within",
)
# Check for any unmatched amenities
unmatched = joined[joined["id"].isna()]
if len(unmatched) > 0:
print(f"WARNING: {len(unmatched)} amenities did not fall inside any arrondissement.")
else:
print("All amenities matched to an arrondissement.")
# Extract arrondissement number from name like "Paris 13e Arrondissement" or "Paris 1er Arrondissement"
def parse_arrondissement_number(name):
m = re.search(r"(\d+)(?:er|e|ère)?\s+Arrondissement", name)
if m:
return int(m.group(1))
return None
# Build output dataframe
result = joined[["osm_id", "amenity_class", "name_right", "id"]].copy()
result.rename(columns={"name_right": "arrondissement_name"}, inplace=True)
result["arrondissement_number"] = result["arrondissement_name"].apply(parse_arrondissement_number)
# Drop the arrondissement id column (not required in output)
result = result[["osm_id", "amenity_class", "arrondissement_number", "arrondissement_name"]]
# Sort by osm_id for a clean output
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"\nOutput written: {len(result)} rows")
print(result.head(10))
print(result.dtypes)Wrote /work/solve.py (1813 chars).
0:32
Let me run the script now.
0:32
Bash
cd /work && python solve.py
Amenities CRS: EPSG:2154
Arrondissements CRS: EPSG:2154
All amenities matched to an arrondissement.
Output written: 85 rows
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
5 9000000005 restaurant 15 Paris 15e Arrondissement
6 9000000006 restaurant 20 Paris 20e Arrondissement
7 9000000007 cafe 6 Paris 6e Arrondissement
8 9000000008 restaurant 18 Paris 18e Arrondissement
9 9000000009 cafe 5 Paris 5e Arrondissement
osm_id int64
amenity_class str
arrondissement_number int64
arrondissement_name str
dtype: object
0:37
Let me verify the output file.
0:37
Bash
ls -la /work/amenity_to_arrondissement.csv
-rw-r--r-- 1 agent agent 3988 Jun 18 01:48 /work/amenity_to_arrondissement.csv
0:37
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
21 9000000019,bakery,9,Paris 9e Arrondissement
22 9000000020,restaurant,19,Paris 19e Arrondissement
23 9000000021,cafe,9,Paris 9e Arrondissement
24 9000000022,restaurant,15,Paris 15e Arrondissement
25 9000000023,bakery,7,Paris 7e Arrondissement
26 9000000024,pharmacy,9,Paris 9e Arrondissement
27 9000000025,library,10,Paris 10e Arrondissement
28 9000000026,bakery,10,Paris 10e Arrondissement
29 9000000027,restaurant,8,Paris 8e Arrondissement
30 9000000028,bakery,18,Paris 18e Arrondissement
31 9000000029,restaurant,15,Paris 15e Arrondissement
32 9000000030,restaurant,11,Paris 11e Arrondissement
33 9000000031,bakery,15,Paris 15e Arrondissement
34 9000000032,bakery,19,Paris 19e Arrondissement
35 9000000033,pharmacy,14,Paris 14e Arrondissement
36 9000000034,pharmacy,15,Paris 15e Arrondissement
37 9000000035,cafe,1,Paris 1er Arrondissement
38 9000000036,restaurant,17,Paris 17e Arrondissement
39 9000000037,cafe,15,Paris 15e Arrondissement
40 9000000038,library,7,Paris 7e Arrondissement
41 9000000039,bakery,15,Paris 15e Arrondissement
42 9000000040,bakery,15,Paris 15e Arrondissement
43 9000000041,library,13,Paris 13e Arrondissement
44 9000000042,pharmacy,19,Paris 19e Arrondissement
45 9000000043,cafe,6,Paris 6e Arrondissement
46 9000000044,bakery,13,Paris 13e Arrondissement
47 9000000045,library,13,Paris 13e Arrondissement
48 9000000046,cafe,18,Paris 18e Arrondissement
49 9000000047,cafe,1,Paris 1er Arrondissement
50 9000000048,pharmacy,15,Paris 15e Arrondissement
51 9000000049,bakery,8,Paris 8e Arrondissement
52 9000000050,bakery,5,Paris 5e Arrondissement
53 9000000051,pharmacy,2,Paris 2e Arrondissement
54 9000000052,bakery,5,Paris 5e Arrondissement
55 9000000053,cafe,1,Paris 1er Arrondissement
56 9000000054,pharmacy,8,Paris 8e Arrondissement
57 9000000055,pharmacy,14,Paris 14e Arrondissement
58 9000000056,cafe,6,Paris 6e Arrondissement
59 9000000057,cafe,16,Paris 16e Arrondissement
60 9000000058,cafe,15,Paris 15e Arrondissement
61 9000000059,cafe,9,Paris 9e Arrondissement
62 9000000060,pharmacy,3,Paris 3e Arrondissement
63 9000000061,pharmacy,16,Paris 16e Arrondissement
64 9000000062,cafe,17,Paris 17e Arrondissement
65 9000000063,cafe,19,Paris 19e Arrondissement
66 9000000064,cafe,18,Paris 18e Arrondissement
67 9000000065,cafe,15,Paris 15e Arrondissement
68 9000000066,pharmacy,16,Paris 16e Arrondissement
69 9000000067,library,10,Paris 10e Arrondissement
70 9000000068,pharmacy,19,Paris 19e Arrondissement
71 9000000069,pharmacy,4,Paris 4e Arrondissement
72 9000000070,pharmacy,13,Paris 13e Arrondissement
73 9000000071,pharmacy,9,Paris 9e Arrondissement
74 9000000072,library,7,Paris 7e Arrondissement
75 9000000073,library,19,Paris 19e Arrondissement
76 9000000074,pharmacy,12,Paris 12e Arrondissement
77 9000000075,pharmacy,7,Paris 7e Arrondissement
78 9000000076,library,10,Paris 10e Arrondissement
79 9000000077,library,14,Paris 14e Arrondissement
80 9000000078,library,19,Paris 19e Arrondissement
81 9000000079,library,7,Paris 7e Arrondissement
82 9000000080,library,5,Paris 5e Arrondissement
83 9000000081,library,5,Paris 5e Arrondissement
84 9000000082,library,12,Paris 12e Arrondissement
85 9000000083,library,1,Paris 1er Arrondissement
86 9000000084,library,13,Paris 13e Arrondissement
0:42
Let me verify everything checks out: 1. Output file `amenity_to_arrondissement.csv` exists ✓ 2. Columns: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string) ✓ 3. 85 rows (one per amenity) ✓ 4. All amenities matched to an arrondissement ✓ 5. Arrondissement numbers are integers ✓ 6. For "Paris 1er Arrondissement", the number is 1 ✓ 7. CRS check: EPSG:2154 (Lambert-93, French projected CRS) — coordinates in the 650k-680k range are correct for Paris metric coordinates ✓ Everything looks good!
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