fio-l1-paris-kml-pois
Map (reference ← swipe → agent)
0:00
A colleague handed me `paris_late_night_pois`. Our internal map server only takes flat GeoJSON or GeoParquet, so please convert it to `paris_pois.geojson`, one row per placemark. Keep `name`, include the source's classification/grouping as a `category` column, and pull each entry's 'last verified' date out of the HTML blurb into a `verified_date` column as an ISO date. We want to flag stale records.
0:00
System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
0:04
Bash
ls -F
ls -F
paris_late_night_pois.kml
0:06
Bash
head -n 20 paris_late_night_pois.kml
head -n 20 paris_late_night_pois.kml
<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Document>
<name>Late-night POIs (RATP night-bus study)</name>
<Folder>
<name>Cafés ouverts tard</name>
<Placemark>
<name>% Arabica</name>
<description><![CDATA[<b>% Arabica</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/0">Voir la fiche</a><br/>Dernière vérification : 2026-01-01]]></description>
<Point>
<coordinates>2.324676,48.854156,0</coordinates>
</Point>
</Placemark>
<Placemark>
<name>1T. rue Scribe</name>
<description><![CDATA[<b>1T. rue Scribe</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/1">Voir la fiche</a><br/>Dernière vérification : 2026-08-14]]></description>
<Point>
<coordinates>2.329774,48.870581,0</coordinates>
</Point>
</Placemark>
0:25
Write
/work/solve.py
content · 71 lines · py
import geopandas as gpd
import pandas as pd
import re
from bs4 import BeautifulSoup
def extract_info(description):
"""
Parses the HTML description to extract category and verified_date.
Example description content:
<b>% Arabica</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/0">Voir la fiche</a><br/>Dernière vérification : 2026-01-01
"""
category = None
verified_date = None
if not description:
return category, verified_date
soup = BeautifulSoup(description, 'html.parser')
text = soup.get_text(separator='|')
# Look for "Catégorie : ..."
# The separator '|' helps isolate the parts.
parts = [p.strip() for p in text.split('|')]
for part in parts:
if 'Catégorie' in part and ':' in part:
category = part.split(':', 1)[1].strip()
if 'Dernière vérification' in part and ':' in part:
date_str = part.split(':', 1)[1].strip()
# Validate if it looks like a date (YYYY-MM-DD)
if re.match(r'\d{4}-\d{2}-\d{2}', date_str):
verified_date = date_str
return category, verified_date
def main():
# Load the KML file
# Note: KML driver might require fiona or lxml
try:
gdf = gpd.read_file('paris_late_night_pois.kml')
except Exception as e:
print(f"Error reading KML: {e}")
return
# Process descriptions
# In KML parsed by geopandas, the description is usually in a column named 'description'
# We check the column names first if it fails
if 'description' not in gdf.columns:
print(f"Available columns: {gdf.columns.tolist()}")
return
# Apply extraction
info_extracted = gdf['description'].apply(extract_info)
gdf['category'] = info_extracted.apply(lambda x: x[0])
gdf['verified_date'] = info_extracted.apply(lambda x: x[1])
# Keep only required columns: name, category, verified_date, and geometry
# We also keep geometry for the GeoJSON
final_gdf = gdf[['name', 'category', 'verified_date', 'geometry']].copy()
# Convert verified_date to datetime objects to ensure it's valid
final_gdf['verified_date'] = pd.to_datetime(final_gdf['verified_date'], errors='coerce')
# Save to GeoJSON
final_gdf.to_file('paris_pois.geojson', driver='GeoJSON')
print("Successfully created paris_pois.geojson")
print(final_gdf.head())
if __name__ == "__main__":
main()
Wrote /work/solve.py (2515 chars).
0:26
Bash
python solve.py
python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 4, in <module>
from bs4 import BeautifulSoup
ModuleNotFoundError: No module named 'bs4'
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Bash
pip install --user beautifulsoup4
pip install --user beautifulsoup4
Collecting beautifulsoup4 Downloading beautifulsoup4-4.15.0-py3-none-any.whl.metadata (3.8 kB) Collecting soupsieve>=1.6.1 (from beautifulsoup4) Downloading soupsieve-2.8.4-py3-none-any.whl.metadata (4.6 kB) Collecting typing-extensions>=4.0.0 (from beautifulsoup4) Downloading typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB) Downloading beautifulsoup4-4.15.0-py3-none-any.whl (109 kB) Downloading soupsieve-2.8.4-py3-none-any.whl (37 kB) Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) Installing collected packages: typing-extensions, soupsieve, beautifulsoup4 Successfully installed beautifulsoup4-4.15.0 soupsieve-2.8.4 typing-extensions-4.15.0
0:37
Bash
python solve.py
python solve.py
/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py:382: UserWarning: More than one layer found in 'paris_late_night_pois.kml': 'Cafés ouverts tard' (default), 'Bibliothèques de nuit', 'Tours et infos touristiques'. Specify layer parameter to avoid this warning.
result = read_func(
Traceback (most recent call last):
File "/work/solve.py", line 70, in <module>
main()
~~~~^^
File "/work/solve.py", line 59, in main
final_gdf = gdf[['name', 'category', 'verified_date', 'geometry']].copy()
~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 4384, in __getitem__
indexer = self.columns._get_indexer_strict(key, "columns")[1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6302, in _get_indexer_strict
self._raise_if_missing(keyarr, indexer, axis_name)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6355, in _raise_if_missing
raise KeyError(f"{not_found} not in index")
KeyError: "['name'] not in index"
0:42
Bash
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_late_night_pois.k...
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_late_night_pois.kml', layer='Cafés ouverts tard'); print(gdf.columns.tolist())"
['id', 'Name', 'description', 'timestamp', 'begin', 'end', 'altitudeMode', 'tessellate', 'extrude', 'visibility', 'drawOrder', 'icon', 'geometry']
0:59
Bash
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_late_night_pois.k...
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_late_night_pois.kml', layer='Cafés ouverts tard'); print(gdf.head(1))"
id Name ... icon geometry 0 None % Arabica ... None POINT Z (2.32468 48.85416 0) [1 rows x 13 columns]
1:22
Write
/work/solve.py
content · 98 lines · py
import geopandas as gpd
import pandas as pd
import re
from bs4 import BeautifulSoup
def extract_info(description):
"""
Parses the HTML description to extract category and verified_date.
Example description content:
<b>% Arabica</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/0">Voir la fiche</a><br/>Dernière vérification : 2026-01-01
"""
category = None
verified_date = None
if not description:
return category, verified_date
soup = BeautifulSoup(description, 'html.parser')
# Use separator to ensure we don't merge words accidentally
text = soup.get_text(separator='|')
parts = [p.strip() for p in text.split('|')]
for part in parts:
# Look for 'Catégorie' (handling potential encoding issues via BeautifulSoup text)
if 'Catégorie' in part and ':' in part:
category = part.split(':', 1)[1].strip()
# Look for 'Dernière vérification'
if 'Dernière vérification' in part and ':' in part:
date_str = part.split(':', 1)[1].strip()
# ISO date format YYYY-MM-DD
match = re.search(r'\d{4}-\d{2}-\d{2}', date_str)
if match:
verified_date = match.group(0)
return category, verified_date
def main():
# The KML has multiple layers. The instructions imply we want all of them
# or at least the POIs. Let's read all layers and concatenate.
import fiona
fiona.drvsupport.supported_drivers['KML'] = 'rw'
layers = fiona.listlayers('paris_late_night_pois.kml')
print(f"Found layers: {layers}")
all_gdfs = []
for layer in layers:
try:
gdf = gpd.read_file('paris_late_night_pois.kml', layer=layer)
all_gdfs.append(gdf)
except Exception as e:
print(f"Error reading layer {layer}: {e}")
if not all_gdfs:
print("No layers could be read.")
return
gdf = pd.concat(all_gdfs, ignore_index=True)
# Check column name for 'Name'. KML often uses 'Name' instead of 'name'.
name_col = None
for col in ['name', 'Name']:
if col in gdf.columns:
name_col = col
break
if name_col is None:
print(f"Could not find name column. Available: {gdf.columns.tolist()}")
return
# Rename for consistency
gdf = gdf.rename(columns={name_col: 'name'})
if 'description' not in gdf.columns:
print(f"No description column found. Available: {gdf.columns.tolist()}")
return
# Apply extraction
info_extracted = gdf['description'].apply(extract_info)
gdf['category'] = info_extracted.apply(lambda x: x[0])
gdf['verified_date'] = info_extracted.apply(lambda x: x[1])
# Convert verified_date to ISO string format (it's already YYYY-MM-DD from regex)
# But ensure we handle NaT/None correctly for the output if needed.
# The requirement says "as an ISO date".
# Keep only required columns
final_gdf = gdf[['name', 'category', 'verified_date', 'geometry']].copy()
# Save to GeoJSON
final_gdf.to_file('paris_pois.geojson', driver='GeoJSON')
print("Successfully created paris_pois.geojson")
print(final_gdf.head())
if __name__ == "__main__":
main()
Wrote /work/solve.py (3351 chars).
1:27
Bash
python solve.py
python solve.py
Found layers: ['Cafés ouverts tard', 'Bibliothèques de nuit', 'Tours et infos touristiques']
Successfully created paris_pois.geojson
name ... geometry
0 % Arabica ... POINT Z (2.32468 48.85416 0)
1 1T. rue Scribe ... POINT Z (2.32977 48.87058 0)
2 1ndix Café ... POINT Z (2.34136 48.88076 0)
3 2 Steps Café ... POINT Z (2.37663 48.86626 0)
4 3234 Café - Trente Deux Trente Quatre ... POINT Z (2.37625 48.85317 0)
[5 rows x 4 columns]
1:32
Bash
head -n 20 paris_pois.geojson
head -n 20 paris_pois.geojson
{
"type": "FeatureCollection",
"name": "paris_pois",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "name": "% Arabica", "category": "Cafés ouverts tard", "verified_date": "2026-01-01" }, "geometry": { "type": "Point", "coordinates": [ 2.324676, 48.854156, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "1T. rue Scribe", "category": "Cafés ouverts tard", "verified_date": "2026-08-14" }, "geometry": { "type": "Point", "coordinates": [ 2.329774, 48.870581, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "1ndix Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-27" }, "geometry": { "type": "Point", "coordinates": [ 2.341361, 48.880764, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "2 Steps Café", "category": "Cafés ouverts tard", "verified_date": "2026-10-12" }, "geometry": { "type": "Point", "coordinates": [ 2.376626, 48.866261, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "3234 Café - Trente Deux Trente Quatre", "category": "Cafés ouverts tard", "verified_date": "2026-05-25" }, "geometry": { "type": "Point", "coordinates": [ 2.376249, 48.853167, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "360 Cafe", "category": "Cafés ouverts tard", "verified_date": "2026-12-10" }, "geometry": { "type": "Point", "coordinates": [ 2.321838, 48.842293, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "3w Cafe Paris", "category": "Cafés ouverts tard", "verified_date": "2026-07-23" }, "geometry": { "type": "Point", "coordinates": [ 2.32753, 48.85156, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "48 Collagen Caf√©", "category": "Cafés ouverts tard", "verified_date": "2026-02-08" }, "geometry": { "type": "Point", "coordinates": [ 2.34046, 48.87484, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "86 Champs Elysées", "category": "Cafés ouverts tard", "verified_date": "2026-09-21" }, "geometry": { "type": "Point", "coordinates": [ 2.303767, 48.871387, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "A la Ville de Paris", "category": "Cafés ouverts tard", "verified_date": "2026-04-06" }, "geometry": { "type": "Point", "coordinates": [ 2.34428, 48.87053, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "A minima cafe", "category": "Cafés ouverts tard", "verified_date": "2026-11-19" }, "geometry": { "type": "Point", "coordinates": [ 2.38129, 48.8453, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "AD DictCafé", "category": "Cafés ouverts tard", "verified_date": "2026-06-04" }, "geometry": { "type": "Point", "coordinates": [ 2.390577, 48.849002, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "ANGIOLO", "category": "Cafés ouverts tard", "verified_date": "2026-01-17" }, "geometry": { "type": "Point", "coordinates": [ 2.331682, 48.854538, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "Abaita", "category": "Cafés ouverts tard", "verified_date": "2026-08-02" }, "geometry": { "type": "Point", "coordinates": [ 2.368446, 48.864406, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "Absinthe Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-15" }, "geometry": { "type": "Point", "coordinates": [ 2.357228, 48.865564, 0.0 ] } },
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