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 start by exploring the working directory to see what data files are available.
0:02
Bash
ls /work/
paris_amenities.gpkg
0:06
Let's first explore what's in the geopackage file to understand the layers and fields.
0:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# List layers
import fiona
layers = fiona.listlayers('paris_amenities.gpkg')
print('Layers:', layers)
for lyr in layers:
gdf = gpd.read_file('paris_amenities.gpkg', layer=lyr)
print(f'\nLayer: {lyr}')
print(f' CRS: {gdf.crs}')
print(f' Rows: {len(gdf)}')
print(f' Columns: {list(gdf.columns)}')
print(f' Geometry type: {gdf.geometry.type.iloc[0]}')
print(f' Sample:')
print(gdf.head(2).to_string())
"
Layers: ['amenities', 'arrondissements']
Layer: amenities
CRS: EPSG:2154
Rows: 85
Columns: ['osm_id', 'amenity_class', 'name', 'geometry']
Geometry type: Point
Sample:
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)
Layer: arrondissements
CRS: EPSG:2154
Rows: 20
Columns: ['id', 'name', 'geometry']
Geometry type: Polygon
Sample:
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))
0:15
Now I understand the data. Let me write a script to: 1. Read the amenities and arrondissements layers 2. Perform a spatial join to assign each amenity point to the arrondissement polygon it falls within 3. Extract arrondissement number from the name (e.g., "Paris 13e Arrondissement" → 13) 4. Write to CSV with columns: osm_id, amenity_class, arrondissement_number, arrondissement_name Let me write the script.
0:15
Write
/work/solve.py
content · 53 lines · py
import geopandas as gpd
import re
# Read 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 already are EPSG:2154)
print(f"Amenities CRS: {amenities.crs}")
print(f"Arrondissements CRS: {arrondissements.crs}")
# Spatial join: each amenity point gets the arrondissement polygon attributes
joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="within")
# For points exactly on boundary, "within" might miss some; also try intersects
# Let's check for unmatched
unmatched = joined[joined["index_right"].isna()]
if len(unmatched) > 0:
print(f"WARNING: {len(unmatched)} amenities had no match with 'within', retrying with 'intersects'")
joined2 = gpd.sjoin(amenities, arrondissements, how="left", predicate="intersects")
joined = joined2
# Extract arrondissement number from the name field
def extract_number(name):
if pd.isna(name):
return None
# Names like "Paris 13e Arrondissement"
m = re.search(r'(\d+)', name)
if m:
return int(m.group(1))
return None
import pandas as pd
pd = pd # already imported via geopandas
joined["arrondissement_number"] = joined["name"].apply(extract_number)
joined["arrondissement_name"] = joined["name"]
# Build output
result = joined[["osm_id", "amenity_class", "arrondissement_number", "arrondissement_name"]].copy()
result["arrondissement_number"] = result["arrondissement_number"].astype("Int64") # nullable int
# Check for any unmatched
null_count = result["arrondissement_number"].isna().sum()
if null_count > 0:
print(f"WARNING: {null_count} amenities have no arrondissement match")
print(f"Total amenities: {len(result)}")
print(result.head(10).to_string())
# Write CSV
result.to_csv("/work/amenity_to_arrondissement.csv", index=False)
print("\nWritten to /work/amenity_to_arrondissement.csv")Wrote /work/solve.py (1950 chars).
0:20
Bash
cd /work && python3 solve.py
Amenities CRS: EPSG:2154 Arrondissements CRS: EPSG:2154
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 36, in <module>
joined["arrondissement_number"] = joined["name"].apply(extract_number)
~~~~~~^^^^^^^^
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'
0:33
The arrondissements layer has columns: `id`, `name`, `geometry`. But after the spatial join, both the left and right side columns are kept. The `name` column from the left (amenities) doesn't exist, but the `name` from the right (arrondissements) should exist. However, since both sides have a `name` column? Actually, amenities has columns: `osm_id`, `amenity_class`, `name`, `geometry`. Arrondissements has: `id`, `name`, `geometry`. So during sjoin, it'll get suffix conflicts. Let's check what the actual columns are after join.
0:33
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
print('Amenities columns:', list(amenities.columns))
print('Arrondissements columns:', list(arrondissements.columns))
joined = gpd.sjoin(amenities, arrondissements, how='left', predicate='within')
print('Joined columns:', list(joined.columns))
print(joined.head(3).to_string())
"
Amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry']
Arrondissements columns: ['id', 'name', 'geometry']
Joined columns: ['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id', 'name_right']
osm_id amenity_class name_left geometry index_right id name_right
0 9000000000 restaurant Le Grand Amour Restaurant POINT (652773.528 6864033.347) 5 39c0e434-e46e-4ac9-b35a-100f804ede99 Paris 10e Arrondissement
1 9000000001 restaurant Talego POINT (652322.91 6861774.142) 15 98f4ac3a-fc4a-40de-b535-2ebfa6f4e202 Paris 4e Arrondissement
2 9000000002 bakery Mamiche POINT (653028.103 6863582.051) 5 39c0e434-e46e-4ac9-b35a-100f804ede99 Paris 10e Arrondissement
0:38
The join works, but the column names got suffixed with `_left` and `_right` because both tables have a column named `name`. I need to use `name_right` for the arrondissement name. Let me fix the script.
0:38
Edit
/work/solve.py
Edited /work/solve.py.
0:42
Now let me also handle the case where `pd` might not be defined properly. Let me fix the script properly.
0:42
Write
/work/solve.py
content · 48 lines · py
import geopandas as gpd
import pandas as pd
import re
# Read layers
amenities = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
arrondissements = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")
print(f"Amenities CRS: {amenities.crs}")
print(f"Arrondissements CRS: {arrondissements.crs}")
# Spatial join: each amenity point gets the arrondissement polygon attributes
joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="within")
# For points exactly on boundary, "within" might miss some; also try intersects
unmatched = joined[joined["index_right"].isna()]
if len(unmatched) > 0:
print(f"WARNING: {len(unmatched)} amenities had no match with 'within', retrying with 'intersects'")
joined = gpd.sjoin(amenities, arrondissements, how="left", predicate="intersects")
# Extract arrondissement number from the name_right field
def extract_number(name):
if pd.isna(name):
return None
# Names like "Paris 13e Arrondissement"
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["arrondissement_number"] = result["arrondissement_number"].astype("Int64") # nullable int
# Check for any unmatched
null_count = result["arrondissement_number"].isna().sum()
if null_count > 0:
print(f"WARNING: {null_count} amenities have no arrondissement match")
print(f"Total amenities: {len(result)}")
print(result.head(10).to_string())
# Write CSV
result.to_csv("/work/amenity_to_arrondissement.csv", index=False)
print("\nWritten to /work/amenity_to_arrondissement.csv")Wrote /work/solve.py (1828 chars).
0:44
Let me run the script now.
0:44
Bash
cd /work && python3 solve.py
Amenities CRS: EPSG:2154
Arrondissements CRS: EPSG:2154
Total amenities: 85
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
Written to /work/amenity_to_arrondissement.csv
0:48
All 85 amenities matched successfully. Let me verify the output CSV.
0:48
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
0:50
The CSV looks good - it has the correct format with header and all 85 amenities. Let me verify: 1. All 85 rows are present (lines 2-86 = 85 rows) 2. Columns: osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string) 3. No null values - every amenity is matched to an arrondissement This looks complete and correct!
0:50