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"
}
0:09
Let me start by examining the input files to understand their structure and schemas.
0:09
Bash
ls /work/
paris_amenities.gpkg
0:19
Let me look at the data to understand what's in the files.
0:19
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# Read the file and see layers
print(gpd.list_layers('paris_amenities.gpkg'))
"
name geometry_type 0 amenities Point 1 arrondissements Polygon
0:26
Great, so we have two layers in the GeoPackage: `amenities` (Point) and `arrondissements` (Polygon). Let me inspect the columns of both.
0:26
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(3).to_string())
print()
print('=== Arrondissements columns ===')
print(arr.columns.tolist())
print(arr.head(3).to_string())
print()
print('=== Amenities CRS ===')
print(amen.crs)
print('=== Arrondissements CRS ===')
print(arr.crs)
"
=== Amenities columns ===
['osm_id', 'amenity_class', 'name', 'geometry']
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)
=== Arrondissements columns ===
['id', 'name', 'geometry']
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 POLYGON ((651390.128 6862244.403, 651327.017 6862265.104, 651072.777 6862346.698, 651056.173 6862352.843, 651033.014 6862295.248, 651019.86 6862259.865, 651045.959 6862247.735, 651082.97 6862229.63, 651043.687 6862160.329, 651008.501 6862097.532, 651007.985 6862096.402, 650981.286 6862047.256, 650967.585 6862022.763, 650965.283 6862019.48, 650962.611 6862015.055, 650928.552 6861950.347, 650895.247 6861882.087, 650890.578 6861867.103, 650878.235 6861825.542, 650876.602 6861820.763, 650873.606 6861811.826, 650866.147 6861798.423, 650864.705 6861795.377, 650851.746 6861763.35, 650825.356 6861699.913, 650789.519 6861610.28, 650785.301 6861604.5, 650736.095 6861540.201, 650735.253 6861536.938, 650734.944 6861532.56, 650734.809 6861523.576, 650724.211 6861520.875, 650680.38 6861509.706, 650634.851 6861499.163, 650605.256 6861473.339, 650589.783 6861457.469, 650489.295 6861395.244, 650445.865 6861369.373, 650436.774 6861363.468, 650414.69 6861349.512, 650403.363 6861342.27, 650340.911 6861297.603, 650305.345 6861271.598, 650249.795 6861229.219, 650139.239 6861144.958, 650138.42 6861145.265, 650138.035 6861144.001, 650108.249 6861121.295, 650106.846 6861121.185, 650106.646 6861120.019, 650075.978 6861097.988, 650031.343 6861069.072, 650013.679 6861059.772, 649970.717 6861035.19, 649964.222 6861031.71, 649897.214 6860995.705, 649895.351 6860995.888, 649845.607 6860970.342, 649892.534 6860935.754, 649903.62 6860926.873, 649916.244 6860916.756, 649942.17 6860896.061, 649950.487 6860889.451, 650002.338 6860847.793, 650017.029 6860836.457, 650021.071 6860833.676, 650033.583 6860823.371, 650046.562 6860814.275, 650110.429 6860777.785, 650173.813 6860744.714, 650295.104 6860682.469, 650314.861 6860672.125, 650316.614 6860671.21, 650321.159 6860668.824, 650333.697 6860662.256, 650438.194 6860607.131, 650448.651 6860601.759, 650542.089 6860554.369, 650544.53 6860553.192, 650546.972 6860552.015, 650561.324 6860544.72, 650568.975 6860540.718, 650571.254 6860539.542, 650573.286 6860538.491, 650648.347 6860499.677, 650651.322 6860498.162, 650698.039 6860474.134, 650733.255 6860455.999, 650745.067 6860450.294, 650765.356 6860440.214, 650786.884 6860429.845, 650794.754 6860425.72, 650804.194 6860420.769, 650814.031 6860414.837, 651083.345 6860277.23, 651138.94 6860248.817, 651145.342 6860245.572, 651187.825 6860224.642, 651317.668 6860160.768, 651323.532 6860276.953, 651328.315 6860276.624, 651332.75 6860276.865, 651337.501 6860277.915, 651341.47 6860279.505, 651347.461 6860283.135, 651352.263 6860287.765, 651355.25 6860291.143, 651359.182 6860297.014, 651363.63 6860307.507, 651392.714 6860387.626, 651396.211 6860396.715, 651425.578 6860476.586, 651581.048 6860889.697, 651582.925 6860894.729, 651601.737 6860943.232, 651603.902 6860948.563, 651611.344 6860967.049, 651627.995 6861011.733, 651630.167 6861017.842, 651674.28 6861126.771, 651687.985 6861159.805, 651690.13 6861165.269, 651695.485 6861178.29, 651699.285 6861187.743, 651756.01 6861325.79, 651766.087 6861346.223, 651774.534 6861375.364, 651796.915 6861445.755, 651800.19 6861455.647, 651802.39 6861462.501, 651808.835 6861483.041, 651814.881 6861502.384, 651817.929 6861511.51, 651821.183 6861520.724, 651851.246 6861612.368, 651869.964 6861675.729, 651873.096 6861686.122, 651886.253 6861727.713, 651910.457 6861755.512, 651882.708 6861771.743, 651866.863 6861783.006, 651780.811 6861849.117, 651769.597 6861863.033, 651713.987 6861934.785, 651644.373 6862027.872, 651627.716 6862046.248, 651620.633 6862052.123, 651538.253 6862120.465, 651501.671 6862150.617, 651524.438 6862174.234, 651571.927 6862223.498, 651406.835 6862277.6, 651402.03 6862263.807, 651397.223 6862265.449, 651390.128 6862244.403))
2 2747f137-3f7b-4ac4-b222-b4ac7acef256 Paris 11e Arrondissement POLYGON ((653870.967 6863517.544, 653863.816 6863514.098, 653761.581 6863467.498, 653737.497 6863456.448, 653630.215 6863396.513, 653627.355 6863394.768, 653620.512 6863391.308, 653601.443 6863382.61, 653576.653 6863371.466, 653575.269 6863370.854, 653568.82 6863367.97, 653565.398 6863366.574, 653563.757 6863365.965, 653502.414 6863338.601, 653453.892 6863314.605, 653451.771 6863313.555, 653331.205 6863240.833, 653362.51 6863212.769, 653403.066 6863131.912, 653400.126 6863117.513, 653400.351 6863114.531, 653400.425 6863113.619, 653401.254 6863110.91, 653402.19 6863107.845, 653410.206 6863086.53, 653415.956 6863070.771, 653420.869 6863057.766, 653437.662 6863016.754, 653443.704 6863002.616, 653448.422 6862989.968, 653463.251 6862952.82, 653463.768 6862951.392, 653468.664 6862937.998, 653473.973 6862924.923, 653481.654 6862903.789, 653482.518 6862901.814, 653484.72 6862896.48, 653490.77 6862881.498, 653508.303 6862836.51, 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6863591.966, 654007.51 6863579.864, 653998.989 6863575.651, 653992.729 6863572.543, 653934.341 6863546.296, 653910.314 6863535.801, 653899.755 6863530.781, 653870.967 6863517.544))
=== Amenities CRS ===
EPSG:2154
=== Arrondissements CRS ===
EPSG:2154
0:39
Both layers have the same CRS (EPSG:2154). Let me do a spatial join to assign each amenity to the arrondissement it falls within. The arrondissements table has columns: `id`, `name`, `geometry` The amenities table has columns: `osm_id`, `amenity_class`, `name`, `geometry` The name column in arrondissements looks like "Paris 13e Arrondissement", "Paris 6e Arrondissement", etc. I need to extract the arrondissement number from the name. Let me check all names first.
0:39
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())
print()
print(arr.shape)
"
['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'] (20, 3)
0:50
Now I have a good understanding of the data. Let me write the script. The amenities layer has columns: osm_id, amenity_class, name, geometry The arrondissements layer has columns: id, name, geometry The output needs: osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string) I need to: 1. Parse the arrondissement name to extract the number. The names are like "Paris 13e Arrondissement", "Paris 1er Arrondissement", etc. 2. Do a spatial join (point in polygon) to match each amenity to its arrondissement. Let me write the script. For parsing: "Paris 13e Arrondissement" -> 13, "Paris 1er Arrondissement" -> 1. I can use a regex to extract the number before "e" or "er" or "ère" etc. Actually let me check all the names more carefully: - Paris 1er Arrondissement - Paris 2e Arrondissement - Paris 3e Arrondissement - etc. So pattern: "Paris (\d+)(?:er|e|ème) Arrondissement" or simpler: extract digits. Let me use: re.search(r'(\d+)', name) to get the first number.
0:50
Write
/work/solve.py
content · 46 lines · py
#!/usr/bin/env python3
"""Spatial join of amenities -> arrondissements, output as CSV."""
import re
import geopandas as gpd
# 1. Read layers
amen = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
arr = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")
# 2. Parse arrondissement number from name strings like
# "Paris 13e Arrondissement" -> 13, "Paris 1er Arrondissement" -> 1
def parse_arr_number(name: str) -> int:
m = re.search(r"(\d+)", name)
if m is None:
raise ValueError(f"Cannot parse arrondissement number from: {name!r}")
return int(m.group(1))
arr["arr_number"] = arr["name"].apply(parse_arr_number)
# 3. Spatial join (point-in-polygon) — EPSG:2154 is already projected (metres)
joined = amen.sjoin(
arr[["arr_number", "name", "geometry"]],
how="left",
predicate="within",
)
# 4. Keep rows that matched (an amenity should always fall in exactly one
# arrondissement, but handle None just in case)
matched = joined[joined["arr_number"].notna()].copy()
# 5. Build the output columns
matched["arrondissement_number"] = matched["arr_number"].astype(int)
matched["arrondissement_name"] = matched["name"]
# 6. Select and order final columns
result = matched[["osm_id", "amenity_class", "arrondissement_number", "arrondissement_name"]].copy()
# 7. Sort for reproducibility
result = result.sort_values(["osm_id"]).reset_index(drop=True)
# 8. 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(10).to_string())Wrote /work/solve.py (1611 chars).
0:55
Let me run the script first.
0:55
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3641, in get_loc
return self._engine.get_loc(casted_key)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "pandas/_libs/index.pyx", line 168, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 197, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 7668, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 7676, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'name'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/work/solve.py", line 34, in <module>
matched["arrondissement_name"] = matched["name"]
~~~~~~~^^^^^^^^
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 4378, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3648, in get_loc
raise KeyError(key) from err
KeyError: 'name'
1:06
The issue is that after the spatial join with `how="left"`, the column names may clash. The right side had `arr_number` and `name`, and the left side also has a `name` column. Since we used `sjoin` with default `lsuffix` and `rsuffix`, the right side's `name` column would get a suffix. Let me check the column names.
1:06
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')
print('amen columns:', amen.columns.tolist())
print('arr columns:', arr.columns.tolist())
# Let's see what sjoin yields
import re
def parse_arr_number(name):
m = re.search(r'(\d+)', name)
return int(m.group(1))
arr['arr_number'] = arr['name'].apply(parse_arr_number)
joined = amen.sjoin(arr[['arr_number', 'name', 'geometry']], how='left', predicate='within')
print('joined columns:', joined.columns.tolist())
print(joined.head(3).to_string())
"
amen columns: ['osm_id', 'amenity_class', 'name', 'geometry']
arr columns: ['id', 'name', 'geometry']
joined columns: ['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'arr_number', 'name_right']
osm_id amenity_class name_left geometry index_right arr_number name_right
0 9000000000 restaurant Le Grand Amour Restaurant POINT (652773.528 6864033.347) 5 10 Paris 10e Arrondissement
1 9000000001 restaurant Talego POINT (652322.91 6861774.142) 15 4 Paris 4e Arrondissement
2 9000000002 bakery Mamiche POINT (653028.103 6863582.051) 5 10 Paris 10e Arrondissement
1:20
Now I see the issue. After the spatial join, the columns from the right (arrondissements) have suffixes. The `name` column from arrondissements becomes `name_right`. Let me fix the script.
1:20
1:20
Edit
/work/solve.py
Edited /work/solve.py.
1:25
Let me also check if there are any amenities that didn't match (no arrondissement), but probably all should match. Let me just run it.
1:25
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
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
1:32
Let me verify the output by checking for any potential issues - missing amenities that didn't match an arrondissement, and also double-check the file output. Let me also verify the file exists and inspect it briefly.
1:32
Bash
cd /work && wc -l amenity_to_arrondissement.csv && head -5 amenity_to_arrondisse...
86 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
1:57
Let me verify: 86 lines = 1 header + 85 data rows. That matches the "Wrote 85 rows" output. The columns are exactly as requested: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string). This looks correct. Let me also check that all amenities matched (no null arr_number):
1:57
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
df = pd.read_csv('amenity_to_arrondissement.csv')
print(f'Rows: {len(df)}')
print(f'Missing arrondissement_number: {df.arrondissement_number.isna().sum()}')
print(f'Missing arrondissement_name: {df.arrondissement_name.isna().sum()}')
print(f'dtypes:\n{df.dtypes}')
"
Rows: 85 Missing arrondissement_number: 0 Missing arrondissement_name: 0 dtypes: osm_id int64 amenity_class str arrondissement_number int64 arrondissement_name str dtype: object
2:03
Everything looks good. Let me verify: 1. ✅ Output file `amenity_to_arrondissement.csv` exists with 85 rows (86 including header) 2. ✅ Columns: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string) 3. ✅ `arrondissement_number` is int64 (integer) 4. ✅ `arrondissement_name` is string 5. ✅ No missing values 6. ✅ Spatial join completed correctly The output is ready.
2:03