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"
}
0:02
Let me first explore the working directory to find the data files.
0:02
Bash
ls /work/
paris_amenities.gpkg
0:05
Let me first explore the data to understand the schema of both layers.
0:05
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import fiona
# List layers in the file
layers = fiona.listlayers('paris_amenities.gpkg')
print('Layers:', layers)
"
Layers: ['amenities', 'arrondissements']
0:10
Great, the file has two layers: `amenities` and `arrondissements`. Let me inspect their schemas and preview the data.
0:10
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:', list(amenities.columns))
print('=== Amenities CRS:', amenities.crs)
print('=== Amenities shape:', amenities.shape)
print(amenities.head(10).to_string())
print()
print('=== Arrondissements columns:', list(arrondissements.columns))
print('=== Arrondissements CRS:', arrondissements.crs)
print('=== Arrondissements shape:', arrondissements.shape)
print(arrondissements.head(10).to_string())
"
=== Amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry']
=== Amenities CRS: EPSG:2154
=== Amenities shape: (85, 4)
osm_id amenity_class name geometry
0 9000000000 restaurant Le Grand Amour Restaurant POINT (652773.528 6864033.347)
1 9000000001 restaurant Talego POINT (652322.91 6861774.142)
2 9000000002 bakery Mamiche POINT (653028.103 6863582.051)
3 9000000003 bakery MyuMyu POINT (653087.577 6858190.655)
4 9000000004 restaurant Rivages POINT (650836.831 6863603.61)
5 9000000005 restaurant Seto POINT (648136.131 6860668.264)
6 9000000006 restaurant Les Petits Campagnards POINT (655325.131 6863817.182)
7 9000000007 cafe Maison Christine Champault POINT (651260.332 6861602.405)
8 9000000008 restaurant Proibido POINT (651294.338 6865384.588)
9 9000000009 cafe Café Zed POINT (652311.708 6860216.575)
=== Arrondissements columns: ['id', 'name', 'geometry']
=== Arrondissements CRS: EPSG:2154
=== Arrondissements shape: (20, 3)
id name geometry
0 0cef9694-93f0-4acf-a44f-f54a01fce855 Paris 13e Arrondissement POLYGON ((651737.858 6858104.126, 651824.872 6858010.988, 651879.152 6857920.158, 651878.75 6857915.913, 651876.549 6857905.524, 651874.039 6857895.96, 651873.83 6857886.732, 651869.177 6857863.153, 651840.43 6857717.337, 651837.951 6857698.276, 651836.206 6857679.043, 651836.072 6857676.253, 651827.749 6857499.967, 651865.954 6857478.445, 651861.715 6857537.514, 652007.983 6857514.829, 652022.343 6857512.587, 652044.198 6857509.204, 652064.249 6857523.804, 652078.81 6857534.392, 652108.197 6857555.621, 652122.233 6857565.813, 652137.895 6857577.181, 652150.631 6857586.427, 652160.464 6857593.574, 652164.401 6857596.41, 652174.877 6857604.007, 652448.903 6857802.594, 652704.918 6857515.663, 652755.98 6857516.17, 653011.548 6857518.574, 653255.012 6857520.876, 653317.012 6857550.132, 653352.58 6857567.059, 653458.445 6857614.255, 653518.475 6857646.231, 654047.126 6857898.644, 654080.73 6857914.756, 654185.04 6857964.792, 654314.057 6858030.591, 654316.608 6858031.894, 654320.242 6858033.744, 654385.141 6858065.146, 654408.676 6858076.536, 654410.858 6858077.587, 654414.189 6858079.195, 654532.979 6858136.562, 654590.02 6858216.753, 654770.688 6858311.892, 654858.156 6858357.945, 654998.184 6858432.302, 655007.112 6858437.048, 655009.206 6858438.155, 655124.743 6858499.117, 655128.135 6858500.903, 655130.033 6858502.412, 655199.438 6858557.551, 655234.729 6858580.162, 655043.631 6858784.339, 654979.865 6858862.37, 654295.066 6859707.248, 654111.087 6859965.421, 653972.59 6860160.28, 653942.12 6860191.39, 653937.655 6860196.596, 653688.174 6860479.88, 653473.234 6860729.416, 653408.541 6860674.915, 653399.768 6860665.478, 653393.077 6860655.725, 653389.382 6860648.059, 653386.152 6860637.889, 653384.725 6860632.096, 653381.16 6860622.217, 653339.29 6860538.488, 653267.204 6860394.179, 653250.264 6860360.423, 653213.476 6860286.674, 653185.345 6860230.602, 653158.334 6860179.803, 652425.223 6859832.457, 652420.2 6859831.376, 652412.091 6859831.609, 652278.043 6859856.018, 652215.385 6859867.532, 652084.264 6859891.51, 652070.241 6859894.684, 652020.712 6859911.083, 651957.626 6859931.988, 651948.695 6859934.92, 651709.008 6860013.683, 651708.543 6860008.082, 651707.418 6859995.493, 651706.834 6859988.759, 651702.013 6859931.711, 651696.369 6859870.166, 651694.565 6859845.773, 651694.17 6859840.595, 651677.685 6859688.526, 651676.85 6859682.517, 651676.272 6859676.562, 651671.272 6859630.646, 651670.689 6859625.024, 651670.097 6859619.169, 651668.309 6859606.408, 651667.79 6859602.276, 651653.068 6859491.735, 651646.962 6859444.439, 651644.911 6859430.034, 651643.806 6859421.659, 651642.132 6859410.097, 651635.353 6859361.361, 651634.786 6859314.574, 651635.245 6859283.291, 651640.611 6859239.257, 651642.693 6859116.556, 651650.138 6859017.285, 651658.095 6858959.085, 651659.049 6858952.861, 651664.504 6858899.987, 651666.097 6858871.852, 651667.291 6858839.773, 651666.85 6858808.886, 651666.008 6858792.448, 651665.266 6858753.691, 651667.354 6858700.299, 651667.665 6858691.835, 651669.696 6858642.224, 651661.737 6858561.162, 651661.87 6858548.007, 651661.904 6858544.126, 651657.629 6858486.751, 651657.151 6858478.727, 651656.647 6858471.147, 651648.708 6858380.144, 651669.105 6858271.837, 651681.214 6858235.041, 651691.566 6858204.954, 651730.162 6858120.792, 651737.858 6858104.126))
1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 Paris 6e Arrondissement
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8 5134a455-e91f-4fdb-8638-c33ee7cb1383 Paris 19e Arrondissement POLYGON ((655001.715 6867044.106, 654913.846 6867069.944, 654873.215 6867081.892, 654554.017 6867074.704, 654475.141 6867072.933, 654253.548 6867067.274, 653928.887 6867059.284, 653836.614 6867057.15, 653832.27 6867046.565, 653828.777 6867035.061, 653827.727 6867030.321, 653827.154 6867026.489, 653826.959 6867021.32, 653827.016 6867009.243, 653829.552 6866949.406, 653830.12 6866893.7, 653831.397 6866844.216, 653832.335 6866772.549, 653832.379 6866769.769, 653834.275 6866646.152, 653836.051 6866594.929, 653837.351 6866554.719, 653837.217 6866548.959, 653838.153 6866510.087, 653839.01 6866475.918, 653856.916 6866453.101, 653902.197 6866395.849, 653905.255 6866382.948, 653912.38 6866374.418, 653938.126 6866343.521, 653940.698 6866340.509, 653935.59 6866336.224, 653907.57 6866312.727, 653868.409 6866276.921, 653846.869 6866254.151, 653839.965 6866241.652, 653820.69 6866211.536, 653816.533 6866205.097, 653812.805 6866190.059, 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0:18
Let me understand the data: 1. **amenities** table has columns: `osm_id`, `amenity_class`, `name`, `geometry` (points, CRS: EPSG:2154) 2. **arrondissements** table has columns: `id`, `name`, `geometry` (polygons, CRS: EPSG:2154) I need to perform a spatial join to find which arrondissement each amenity falls inside, then output to CSV with columns: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string). The arrondissement names are like "Paris 13e Arrondissement". I need to extract the number from the name. Let me look at the names more carefully. From the output I can see: - "Paris 13e Arrondissement" - "Paris 6e Arrondissement" - "Paris 19e Arrondissement" - "Paris 5e Arrondissement" So the pattern is "Paris Xe Arrondissement" where X is the number. I'll need to extract the numeric part. Let me write a Python script to do the spatial join.
0:18
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
arr = gpd.read_file('paris_amenities.gpkg', layer='arrondissements')
print(arr['name'].tolist())
"
['Paris 13e Arrondissement', 'Paris 6e Arrondissement', 'Paris 11e Arrondissement', 'Paris 3e Arrondissement', 'Paris 2e Arrondissement', 'Paris 10e Arrondissement', 'Paris 20e Arrondissement', 'Paris 8e Arrondissement', 'Paris 19e Arrondissement', 'Paris 5e Arrondissement', 'Paris 17e Arrondissement', 'Paris 9e Arrondissement', 'Paris 16e Arrondissement', 'Paris 14e Arrondissement', 'Paris 12e Arrondissement', 'Paris 4e Arrondissement', 'Paris 18e Arrondissement', 'Paris 7e Arrondissement', 'Paris 1er Arrondissement', 'Paris 15e Arrondissement']
0:25
Now I need to do a spatial join between the amenities (points) and arrondissements (polygons) to assign each amenity to its containing arrondissement. I'll extract the arrondissement number from the name (e.g., "Paris 13e Arrondissement" -> 13, "Paris 1er Arrondissement" -> 1). Let me write the Python script.
0:25
Write
/work/solve.py
content · 26 lines · py
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')
# Both are in EPSG:2154, so spatial join works directly
joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')
# Extract arrondissement number from name (e.g. 'Paris 13e Arrondissement' -> 13, 'Paris 1er Arrondissement' -> 1)
def parse_arrondissement_number(name):
match = re.search(r'(\d+)', name)
return int(match.group(1)) if match else None
joined['arrondissement_number'] = joined['name_right'].apply(parse_arrondissement_number)
joined['arrondissement_name'] = joined['name_right']
# Build output DataFrame with required columns
result = joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()
# Write to CSV (one row per amenity, no geometry)
result.to_csv('/work/amenity_to_arrondissement.csv', index=False)
print(f"Wrote {len(result)} rows to amenity_to_arrondissement.csv")
print(result.head(10).to_string())Wrote /work/solve.py (1088 chars).
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Let me run the script.
0:28
Bash
cd /work && python solve.py
Wrote 85 rows to 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
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
0:34
The join worked well. Let me verify that all 85 amenities found a match (none are null).
0:34
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('Null arrondissement_number:', df['arrondissement_number'].isna().sum())
print('Null arrondissement_name:', df['arrondissement_name'].isna().sum())
print()
print(df.to_string())
"
Total rows: 85
Null arrondissement_number: 0
Null arrondissement_name: 0
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
10 9000000010 restaurant 17 Paris 17e Arrondissement
11 9000000011 restaurant 6 Paris 6e Arrondissement
12 9000000012 restaurant 8 Paris 8e Arrondissement
13 9000000013 restaurant 1 Paris 1er Arrondissement
14 9000000014 restaurant 10 Paris 10e Arrondissement
15 9000000015 cafe 15 Paris 15e Arrondissement
16 9000000016 cafe 12 Paris 12e Arrondissement
17 9000000017 bakery 15 Paris 15e Arrondissement
18 9000000018 cafe 3 Paris 3e Arrondissement
19 9000000019 bakery 9 Paris 9e Arrondissement
20 9000000020 restaurant 19 Paris 19e Arrondissement
21 9000000021 cafe 9 Paris 9e Arrondissement
22 9000000022 restaurant 15 Paris 15e Arrondissement
23 9000000023 bakery 7 Paris 7e Arrondissement
24 9000000024 pharmacy 9 Paris 9e Arrondissement
25 9000000025 library 10 Paris 10e Arrondissement
26 9000000026 bakery 10 Paris 10e Arrondissement
27 9000000027 restaurant 8 Paris 8e Arrondissement
28 9000000028 bakery 18 Paris 18e Arrondissement
29 9000000029 restaurant 15 Paris 15e Arrondissement
30 9000000030 restaurant 11 Paris 11e Arrondissement
31 9000000031 bakery 15 Paris 15e Arrondissement
32 9000000032 bakery 19 Paris 19e Arrondissement
33 9000000033 pharmacy 14 Paris 14e Arrondissement
34 9000000034 pharmacy 15 Paris 15e Arrondissement
35 9000000035 cafe 1 Paris 1er Arrondissement
36 9000000036 restaurant 17 Paris 17e Arrondissement
37 9000000037 cafe 15 Paris 15e Arrondissement
38 9000000038 library 7 Paris 7e Arrondissement
39 9000000039 bakery 15 Paris 15e Arrondissement
40 9000000040 bakery 15 Paris 15e Arrondissement
41 9000000041 library 13 Paris 13e Arrondissement
42 9000000042 pharmacy 19 Paris 19e Arrondissement
43 9000000043 cafe 6 Paris 6e Arrondissement
44 9000000044 bakery 13 Paris 13e Arrondissement
45 9000000045 library 13 Paris 13e Arrondissement
46 9000000046 cafe 18 Paris 18e Arrondissement
47 9000000047 cafe 1 Paris 1er Arrondissement
48 9000000048 pharmacy 15 Paris 15e Arrondissement
49 9000000049 bakery 8 Paris 8e Arrondissement
50 9000000050 bakery 5 Paris 5e Arrondissement
51 9000000051 pharmacy 2 Paris 2e Arrondissement
52 9000000052 bakery 5 Paris 5e Arrondissement
53 9000000053 cafe 1 Paris 1er Arrondissement
54 9000000054 pharmacy 8 Paris 8e Arrondissement
55 9000000055 pharmacy 14 Paris 14e Arrondissement
56 9000000056 cafe 6 Paris 6e Arrondissement
57 9000000057 cafe 16 Paris 16e Arrondissement
58 9000000058 cafe 15 Paris 15e Arrondissement
59 9000000059 cafe 9 Paris 9e Arrondissement
60 9000000060 pharmacy 3 Paris 3e Arrondissement
61 9000000061 pharmacy 16 Paris 16e Arrondissement
62 9000000062 cafe 17 Paris 17e Arrondissement
63 9000000063 cafe 19 Paris 19e Arrondissement
64 9000000064 cafe 18 Paris 18e Arrondissement
65 9000000065 cafe 15 Paris 15e Arrondissement
66 9000000066 pharmacy 16 Paris 16e Arrondissement
67 9000000067 library 10 Paris 10e Arrondissement
68 9000000068 pharmacy 19 Paris 19e Arrondissement
69 9000000069 pharmacy 4 Paris 4e Arrondissement
70 9000000070 pharmacy 13 Paris 13e Arrondissement
71 9000000071 pharmacy 9 Paris 9e Arrondissement
72 9000000072 library 7 Paris 7e Arrondissement
73 9000000073 library 19 Paris 19e Arrondissement
74 9000000074 pharmacy 12 Paris 12e Arrondissement
75 9000000075 pharmacy 7 Paris 7e Arrondissement
76 9000000076 library 10 Paris 10e Arrondissement
77 9000000077 library 14 Paris 14e Arrondissement
78 9000000078 library 19 Paris 19e Arrondissement
79 9000000079 library 7 Paris 7e 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
0:40
The file has been successfully created. All 85 amenities are matched to their arrondissements with no null values. Let me confirm the file is properly written.
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