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).
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
paris_amenities.gpkg
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Bash
ogrinfo /work/paris_amenities.gpkg
INFO: Open of `/work/paris_amenities.gpkg'
using driver `GPKG' successful.
1: amenities (Point)
2: arrondissements (Polygon)
0:31
Write
/work/inspect_data.py
content · 45 lines · py
import geopandas as gpd
def main():
# Load the data
gdf = gpd.read_file('/work/paris_amenities.gpkg')
# Inspect layers and column names
print("Layers in GPKG:")
print(gdf.layers) # Wait, read_file might load everything or I should use pyogrio to see layers if it's a multi-layer file
# Actually, gpd.read_file usually takes a layer argument if it's multi-layer
# Let's try to read them separately
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Amenities columns:", amenities.columns.tolist())
print("Arrondissements columns:", arrondondissements.columns.tolist())
# Spatial join
# We want to tag each amenity with its arrondissement
# Ensure they are in the same CRS
if amenities.crs != arrondissements.crs:
amenities = amenities.to_crs(arrondissements.crs)
# Join
joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')
# Check columns for the requested output:
# osm_id, amenity_class, arrondissement_number (int), arrondissement_name (str)
# Let's see what columns we actually have after sjoin
print("Joined columns:", joined.columns.tolist())
# We need to map the actual column names to the requested ones.
# Based on the prompt, I need to identify which columns correspond to:
# osm_id -> ?
# amenity_class -> ?
# arrondissement_number -> ?
# arrondissement_name -> ?
# I'll assume standard OSM/GeoJSON/GPKG naming or check in the print above.
# Let's refine the script after seeing the column names.
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (1731 chars).
0:33
Bash
python /work/inspect_data.py
Layers in GPKG:
/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(
Traceback (most recent call last):
File "/work/inspect_data.py", line 44, in <module>
main()
~~~~^^
File "/work/inspect_data.py", line 9, in main
print(gdf.layers) # Wait, read_file might load everything or I should use pyogrio to see layers if it's a multi-layer file
^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'GeoDataFrame' object has no attribute 'layers'
0:42
Write
/work/inspect_data.py
content · 12 lines · py
import geopandas as gpd
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Amenities columns:", amenities.columns.tolist())
print("Arrondissements columns:", arrondissements.columns.tolist())
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (379 chars).
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Bash
python /work/inspect_data.py
Amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry'] Arrondissements columns: ['id', 'name', 'geometry']
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(no output)
Bash
# Wait, I noticed 'arrondissements' columns are ['id', 'name', 'geometry'].
# Wait, I noticed 'arrondissements' columns are ['id', 'name', 'geometry']. # The prompt asks for 'arrondissement_number' (integer) and 'arrondissement_name' (string). # Let's check if 'id' in arrondissements corresponds to the number. # Often in Paris, the arrondissement index is 1-19. # Let's check some values.
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Write
/work/inspect_arrondissements.py
content · 12 lines · py
import geopandas as gpd
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Arrondissements head:")
print(arrondissements.head())
if __name__ == "__main__":
main()
Wrote /work/inspect_arrondissements.py (316 chars).
1:03
Bash
python /work/inspect_arrondissements.py
Arrondissements head:
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]
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Write
/work/inspect_arrondissements.py
content · 12 lines · py
import geopandas as gpd
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Arrondissements content:")
print(arrondissements)
if __name__ == "__main__":
main()
Wrote /work/inspect_arrondissements.py (312 chars).
1:21
Bash
python /work/inspect_arrondissements.py
Arrondissements content:
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 39c0e434-e46e-4ac9-b35a-100f804ede99 ... POLYGON ((653563.877 6865098.936, 653561.716 6...
6 4687f7be-db17-41c3-a9d0-09afa5523060 ... POLYGON ((655893.476 6861249.807, 655915.942 6...
7 4e7c04fe-3c0d-4401-b79d-ce3e2395e5fd ... POLYGON ((650659.118 6865039.446, 650178.081 6...
8 5134a455-e91f-4fdb-8638-c33ee7cb1383 ... POLYGON ((655001.715 6867044.106, 654913.846 6...
9 5537e76e-52db-4342-938d-a91168f4e6a7 ... POLYGON ((651800.19 6861455.647, 651796.915 68...
10 571cb253-40c3-4761-b663-d30deedf713f ... POLYGON ((650838.564 6866750.339, 650841.844 6...
11 75e0a62a-117b-46e6-8782-a4b225ce37e4 ... POLYGON ((651929.446 6864969.291, 651739.736 6...
12 7caa32e9-d051-48f2-87ae-29d09104547e ... POLYGON ((647279.38 6864482.674, 647276.526 68...
13 818e7bcb-32e9-4567-919a-c44153254398 ... POLYGON ((649486.714 6859519.417, 649424.591 6...
14 8a6c934a-8534-4e30-a9e5-e827de7a9891 ... POLYGON ((653937.655 6860196.596, 653942.12 68...
15 98f4ac3a-fc4a-40de-b535-2ebfa6f4e202 ... POLYGON ((652370.492 6862619.641, 652335.937 6...
16 dcc8aaa3-2adb-424f-a745-b9a23163f7c0 ... POLYGON ((653827.727 6867030.321, 653828.777 6...
17 ea79b3d9-590d-450e-8da6-30cb1b6fcc66 ... POLYGON ((650807.203 6862456.265, 650721.566 6...
18 ed5166f4-7111-4e42-bcbe-0630271cc71e ... POLYGON ((652048.084 6861992.57, 652052.515 68...
19 f22f8b89-a6ec-449f-bc9a-cf3c7644d0e0 ... POLYGON ((647714.89 6861965.77, 647539.402 686...
[20 rows x 3 columns]
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Write
/work/inspect_arrondissements_v2.py
content · 13 lines · py
import geopandas as gpd
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Arrondissements columns:", arrondissements.columns.tolist())
print("Arrondissements head:")
print(arrondissements.head())
if __name__ == "__main__":
main()
Wrote /work/inspect_arrondissements_v2.py (388 chars).
1:38
Bash
python /work/inspect_arrondissements_v2.py
Arrondissements columns: ['id', 'name', 'geometry']
Arrondissements head:
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]
1:51
Write
/work/inspect_arrondissements_v3.py
content · 12 lines · py
import geopandas as gpd
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print("Arrondissements content:")
print(arrondissements[['id', 'name']])
if __name__ == "__main__":
main()
Wrote /work/inspect_arrondissements_v3.py (328 chars).
1:53
Bash
python /work/inspect_arrondissements_v3.py
Arrondissements content:
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
2:05
Write
/work/solve.py
content · 53 lines · py
import geopandas as gpd
import re
def extract_arrondissement_number(name):
# Extract the number from "Paris 13e Arrondissement"
match = re.search(r'(\d+)', name)
if match:
return int(match.group(1))
return None
def main():
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
# Process arrondissements to get number and name
# The prompt asks for arrondissement_number (integer) and arrondissement_name (string)
# arrondissement_name should be the name (e.g., "Paris 13e Arrondissement" or similar)
# Let's see what name they want. "arrondissement_name (string)"
arrondissements['arrondissement_number'] = arrondissements['name'].apply(extract_arrondissement_number)
arrondissements['arrondissement_name'] = arrondissements['name']
# Spatial join
if amenities.crs != arrondissements.crs:
amenities = amenities.to_crs(arrondissements.crs)
# join amenities to arrondissements
# We want to tag each amenity with its arrondissement
joined = gpd.sjoin(amenities, arrondissements[['arrondissement_number', 'arrondissement_name', 'geometry']], how='left', predicate='within')
# Select and rename columns as requested
# osm_id, amenity_class, arrondissement_number, arrondissement_name
result = joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']]
# Ensure types are correct
result['arrondissement_number'] = result['arrondissement_number'].astype(int)
# If there are NaNs (amenities outside any arrondissement), astype(int) will fail.
# But in Paris, most amenities should be inside.
# Let's check for NaNs first.
if result['arrondissement_number'].isnull().any():
print("Warning: some amenities are not in an arrondissement.")
# For the sake of the task, we'll drop them or handle them.
# Usually, in these tasks, everything is covered.
result = result.dropna(subset=['arrondissement_number'])
result['arrondissement_number'] = result['arrondissement_number'].astype(int)
result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
print("Successfully wrote amenity_to_arrondissement.csv")
if __name__ == "__main__":
main()
Wrote /work/solve.py (2359 chars).
2:06
Bash
python /work/solve.py
Successfully wrote amenity_to_arrondissement.csv
2:12
Bash
head -n 5 /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
2:13
Bash
python -c "import pandas as pd; df = pd.read_csv('/work/amenity_to_arrondissemen...
<class 'pandas.DataFrame'> RangeIndex: 85 entries, 0 to 84 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 osm_id 85 non-null int64 1 amenity_class 85 non-null str 2 arrondissement_number 85 non-null int64 3 arrondissement_name 85 non-null str dtypes: int64(2), str(2) memory usage: 5.3 KB None
2:22