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 files are available.
0:03
Bash
ls -la /work/
total 208 drwxr-xr-x 1 agent agent 40 Jun 16 04:23 . drwxr-xr-x 1 root root 14 Jun 16 04:23 .. -rw-r--r-- 1 agent agent 212992 Jun 16 04:23 paris_amenities.gpkg
0:09
Let me understand what's available. There's a GeoPackage file `paris_amenities.gpkg`. I need to explore its contents to find the amenities and arrondissements data.
0:09
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# List all layers
layers = gpd.list_layers('paris_amenities.gpkg')
print(layers)
"
name geometry_type 0 amenities Point 1 arrondissements Polygon
0:21
Great, we have two layers: "amenities" (Point) and "arrondissements" (Polygon). Let's inspect them.
0:21
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
print('Amenities columns:', amenities.columns.tolist())
print('Amenities shape:', amenities.shape)
print()
print(amenities.head())
print()
print('Arrondissements columns:', arrondissements.columns.tolist())
print('Arrondissements shape:', arrondissements.shape)
print()
print(arrondissements.head())
print()
print('CRS amenities:', amenities.crs)
print('CRS arrondissements:', arrondissements.crs)
"
Amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry']
Amenities shape: (85, 4)
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]
Arrondissements columns: ['id', 'name', 'geometry']
Arrondissements shape: (20, 3)
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]
CRS amenities: EPSG:2154
CRS arrondissements: EPSG:2154
0:25
Let me explore the arrondissements data more to understand the "name" field - it likely has the arrondissement number and name. Let me look at all arrondissements names to understand them.
0:25
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']].to_string())
"
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
Now I need to do a spatial join between amenities (points) and arrondissements (polygons) to find which arrondissement each amenity falls inside. I'll extract the arrondissement number from the name (e.g., "Paris 13e Arrondissement" → 13, "Paris 1er Arrondissement" → 1). Then write the result as CSV with columns: osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string).
0:33
Bash
cd /work && python3 << 'EOF'
cd /work && python3 << 'EOF'
import geopandas as gpd
import re
# Read data
amenities = gpd.read_file('paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
# CRS is already EPSG:2154 (same for both), so spatial join works directly
joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')
# Extract arrondissement number from name
def extract_number(name):
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']
# Build output
result = joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()
result = result.sort_values('osm_id').reset_index(drop=True)
# Write CSV
result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
print(result)
print(f"\nWritten {len(result)} rows to /work/amenity_to_arrondissement.csv")
EOF
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 .. ... ... ... ... 80 9000000080 library 5 Paris 5e Arrondissement 81 9000000081 library 5 Paris 5e Arrondissement 82 9000000082 library 12 Paris 12e Arrondissement 83 9000000083 library 1 Paris 1er Arrondissement 84 9000000084 library 13 Paris 13e Arrondissement [85 rows x 4 columns] Written 85 rows to /work/amenity_to_arrondissement.csv
0:37
Let me verify the output is correct by checking a few things: 1. All amenities are joined (85 rows) 2. No null arrondissements (all amenities should be inside Paris) 3. The CSV looks clean Let me double-check the output file.
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
0:43
The CSV has been created successfully with all 85 amenities joined to their respective arrondissements. Let me verify a few things: 1. Each amenity (osm_id) appears once - yes, 85 rows. 2. The columns are: osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string) - correct. 3. All arrondissements are covered - let me quickly check if any amenity fell outside all arrondissements (NaN values). Let me check that none are null.
0:43
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_csv('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('Arrondissement numbers present:', sorted(df['arrondissement_number'].unique()))
print()
print('All columns:', df.columns.tolist())
print('dtypes:', df.dtypes.to_dict())
"
Total rows: 85
Missing arrondissement_number: 0
Missing arrondissement_name: 0
Arrondissement numbers present: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9), np.int64(10), np.int64(11), np.int64(12), np.int64(13), np.int64(14), np.int64(15), np.int64(16), np.int64(17), np.int64(18), np.int64(19), np.int64(20)]
All columns: ['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']
dtypes: {'osm_id': dtype('int64'), 'amenity_class': <StringDtype(na_value=nan)>, 'arrondissement_number': dtype('int64'), 'arrondissement_name': <StringDtype(na_value=nan)>}
0:54
The join is complete and the CSV has been written successfully. All 85 amenities have been assigned to their respective arrondissements, with the correct columns and integer type for `arrondissement_number`.
0:54