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": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:02
Let me start by reading the input file to understand its structure.
0:02
Bash
ls -la /work/
total 20 drwxr-xr-x 1 agent agent 50 Jun 18 00:54 . drwxr-xr-x 1 root root 14 Jun 18 00:54 .. -rw-r--r-- 1 agent agent 18305 Jun 18 00:54 paris_late_night_pois.kml
0:07
Let's look at the KML file to understand its structure.
0:07
Read
/work/paris_late_night_pois.kml
1 <?xml version="1.0" encoding="UTF-8"?>
2 <kml xmlns="http://www.opengis.net/kml/2.2">
3 <Document>
4 <name>Late-night POIs (RATP night-bus study)</name>
5 <Folder>
6 <name>Cafés ouverts tard</name>
7 <Placemark>
8 <name>% Arabica</name>
9 <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>
10 <Point>
11 <coordinates>2.324676,48.854156,0</coordinates>
12 </Point>
13 </Placemark>
14 <Placemark>
15 <name>1T. rue Scribe</name>
16 <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>
17 <Point>
18 <coordinates>2.329774,48.870581,0</coordinates>
19 </Point>
20 </Placemark>
21 <Placemark>
22 <name>1ndix Café</name>
23 <description><![CDATA[<b>1ndix Café</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/2">Voir la fiche</a><br/>Dernière vérification : 2026-03-27]]></description>
24 <Point>
25 <coordinates>2.341361,48.880764,0</coordinates>
26 </Point>
27 </Placemark>
28 <Placemark>
29 <name>2 Steps Café</name>
30 <description><![CDATA[<b>2 Steps Café</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/3">Voir la fiche</a><br/>Dernière vérification : 2026-10-12]]></description>
31 <Point>
32 <coordinates>2.376626,48.866261,0</coordinates>
33 </Point>
34 </Placemark>
35 <Placemark>
36 <name>3234 Café - Trente Deux Trente Quatre</name>
37 <description><![CDATA[<b>3234 Café - Trente Deux Trente Quatre</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/4">Voir la fiche</a><br/>Dernière vérification : 2026-05-25]]></description>
38 <Point>
39 <coordinates>2.376249,48.853167,0</coordinates>
40 </Point>
41 </Placemark>
42 <Placemark>
43 <name>360 Cafe</name>
44 <description><![CDATA[<b>360 Cafe</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/5">Voir la fiche</a><br/>Dernière vérification : 2026-12-10]]></description>
45 <Point>
46 <coordinates>2.321838,48.842293,0</coordinates>
47 </Point>
48 </Placemark>
49 <Placemark>
50 <name>3w Cafe Paris</name>
51 <description><![CDATA[<b>3w Cafe Paris</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/6">Voir la fiche</a><br/>Dernière vérification : 2026-07-23]]></description>
52 <Point>
53 <coordinates>2.327530,48.851560,0</coordinates>
54 </Point>
55 </Placemark>
56 <Placemark>
57 <name>48 Collagen Café</name>
58 <description><![CDATA[<b>48 Collagen Caf√©</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/7">Voir la fiche</a><br/>Dernière vérification : 2026-02-08]]></description>
59 <Point>
60 <coordinates>2.340460,48.874840,0</coordinates>
61 </Point>
62 </Placemark>
63 <Placemark>
64 <name>86 Champs Elysées</name>
65 <description><![CDATA[<b>86 Champs Elysées</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/8">Voir la fiche</a><br/>Dernière vérification : 2026-09-21]]></description>
66 <Point>
67 <coordinates>2.303767,48.871387,0</coordinates>
68 </Point>
69 </Placemark>
70 <Placemark>
71 <name>A la Ville de Paris</name>
72 <description><![CDATA[<b>A la Ville de Paris</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/9">Voir la fiche</a><br/>Dernière vérification : 2026-04-06]]></description>
73 <Point>
74 <coordinates>2.344280,48.870530,0</coordinates>
75 </Point>
76 </Placemark>
77 <Placemark>
78 <name>A minima cafe</name>
79 <description><![CDATA[<b>A minima cafe</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/10">Voir la fiche</a><br/>Dernière vérification : 2026-11-19]]></description>
80 <Point>
81 <coordinates>2.381290,48.845300,0</coordinates>
82 </Point>
83 </Placemark>
84 <Placemark>
85 <name>AD DictCafé</name>
86 <description><![CDATA[<b>AD DictCafé</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/11">Voir la fiche</a><br/>Dernière vérification : 2026-06-04]]></description>
87 <Point>
88 <coordinates>2.390577,48.849002,0</coordinates>
89 </Point>
90 </Placemark>
91 <Placemark>
92 <name>ANGIOLO</name>
93 <description><![CDATA[<b>ANGIOLO</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/12">Voir la fiche</a><br/>Dernière vérification : 2026-01-17]]></description>
94 <Point>
95 <coordinates>2.331682,48.854538,0</coordinates>
96 </Point>
97 </Placemark>
98 <Placemark>
99 <name>Abaita</name>
100 <description><![CDATA[<b>Abaita</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/13">Voir la fiche</a><br/>Dernière vérification : 2026-08-02]]></description>
101 <Point>
102 <coordinates>2.368446,48.864406,0</coordinates>
103 </Point>
104 </Placemark>
105 <Placemark>
106 <name>Absinthe Café</name>
107 <description><![CDATA[<b>Absinthe Café</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/14">Voir la fiche</a><br/>Dernière vérification : 2026-03-15]]></description>
108 <Point>
109 <coordinates>2.357228,48.865564,0</coordinates>
110 </Point>
111 </Placemark>
112 <Placemark>
113 <name>Acai & You</name>
114 <description><![CDATA[<b>Acai & You</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/15">Voir la fiche</a><br/>Dernière vérification : 2026-10-28]]></description>
115 <Point>
116 <coordinates>2.339001,48.854247,0</coordinates>
117 </Point>
118 </Placemark>
119 <Placemark>
120 <name>Adulis Cafe Abyssinien</name>
121 <description><![CDATA[<b>Adulis Cafe Abyssinien</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/16">Voir la fiche</a><br/>Dernière vérification : 2026-05-13]]></description>
122 <Point>
123 <coordinates>2.346929,48.851763,0</coordinates>
124 </Point>
125 </Placemark>
126 <Placemark>
127 <name>After Place</name>
128 <description><![CDATA[<b>After Place</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/17">Voir la fiche</a><br/>Dernière vérification : 2026-12-26]]></description>
129 <Point>
130 <coordinates>2.336121,48.864266,0</coordinates>
131 </Point>
132 </Placemark>
133 <Placemark>
134 <name>After Place</name>
135 <description><![CDATA[<b>After Place</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/18">Voir la fiche</a><br/>Dernière vérification : 2026-07-11]]></description>
136 <Point>
137 <coordinates>2.351101,48.862294,0</coordinates>
138 </Point>
139 </Placemark>
140 <Placemark>
141 <name>After Place</name>
142 <description><![CDATA[<b>After Place</b><br/>Catégorie : Cafés ouverts tard<br/><a href="https://example.org/poi/19">Voir la fiche</a><br/>Dernière vérification : 2026-02-24]]></description>
143 <Point>
144 <coordinates>2.361560,48.869500,0</coordinates>
145 </Point>
146 </Placemark>
147 </Folder>
148 <Folder>
149 <name>Bibliothèques de nuit</name>
150 <Placemark>
151 <name>AUP Housing</name>
152 <description><![CDATA[<b>AUP Housing</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/20">Voir la fiche</a><br/>Dernière vérification : 2026-09-09]]></description>
153 <Point>
154 <coordinates>2.306008,48.861754,0</coordinates>
155 </Point>
156 </Placemark>
157 <Placemark>
158 <name>Academie de Medical</name>
159 <description><![CDATA[<b>Academie de Medical</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/21">Voir la fiche</a><br/>Dernière vérification : 2026-04-22]]></description>
160 <Point>
161 <coordinates>2.334208,48.856342,0</coordinates>
162 </Point>
163 </Placemark>
164 <Placemark>
165 <name>Ancêtres Italiens</name>
166 <description><![CDATA[<b>Ancêtres Italiens</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/22">Voir la fiche</a><br/>Dernière vérification : 2026-11-07]]></description>
167 <Point>
168 <coordinates>2.363314,48.873246,0</coordinates>
169 </Point>
170 </Placemark>
171 <Placemark>
172 <name>Apfelbaum Library</name>
173 <description><![CDATA[<b>Apfelbaum Library</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/23">Voir la fiche</a><br/>Dernière vérification : 2026-06-20]]></description>
174 <Point>
175 <coordinates>2.330685,48.849518,0</coordinates>
176 </Point>
177 </Placemark>
178 <Placemark>
179 <name>Archives de l'AP-HP</name>
180 <description><![CDATA[<b>Archives de l'AP-HP</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/24">Voir la fiche</a><br/>Dernière vérification : 2026-01-05]]></description>
181 <Point>
182 <coordinates>2.366407,48.857094,0</coordinates>
183 </Point>
184 </Placemark>
185 <Placemark>
186 <name>Arthur Rimbaud Library</name>
187 <description><![CDATA[<b>Arthur Rimbaud Library</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/25">Voir la fiche</a><br/>Dernière vérification : 2026-08-18]]></description>
188 <Point>
189 <coordinates>2.355874,48.856134,0</coordinates>
190 </Point>
191 </Placemark>
192 <Placemark>
193 <name>Association Bibliotheque Braille Enfantine</name>
194 <description><![CDATA[<b>Association Bibliotheque Braille Enfantine</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/26">Voir la fiche</a><br/>Dernière vérification : 2026-03-03]]></description>
195 <Point>
196 <coordinates>2.372404,48.868038,0</coordinates>
197 </Point>
198 </Placemark>
199 <Placemark>
200 <name>Association Francaise de Therapie</name>
201 <description><![CDATA[<b>Association Francaise de Therapie</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/27">Voir la fiche</a><br/>Dernière vérification : 2026-10-16]]></description>
202 <Point>
203 <coordinates>2.308679,48.870960,0</coordinates>
204 </Point>
205 </Placemark>
206 <Placemark>
207 <name>Attractive</name>
208 <description><![CDATA[<b>Attractive</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/28">Voir la fiche</a><br/>Dernière vérification : 2026-05-01]]></description>
209 <Point>
210 <coordinates>2.326704,48.866369,0</coordinates>
211 </Point>
212 </Placemark>
213 <Placemark>
214 <name>BNF</name>
215 <description><![CDATA[<b>BNF</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/29">Voir la fiche</a><br/>Dernière vérification : 2026-12-14]]></description>
216 <Point>
217 <coordinates>2.338076,48.867811,0</coordinates>
218 </Point>
219 </Placemark>
220 <Placemark>
221 <name>BPI Bibliotheque Publique D Information</name>
222 <description><![CDATA[<b>BPI Bibliotheque Publique D Information</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/30">Voir la fiche</a><br/>Dernière vérification : 2026-07-27]]></description>
223 <Point>
224 <coordinates>2.352378,48.859478,0</coordinates>
225 </Point>
226 </Placemark>
227 <Placemark>
228 <name>BPI-Beaubourg</name>
229 <description><![CDATA[<b>BPI-Beaubourg</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/31">Voir la fiche</a><br/>Dernière vérification : 2026-02-12]]></description>
230 <Point>
231 <coordinates>2.351867,48.860532,0</coordinates>
232 </Point>
233 </Placemark>
234 <Placemark>
235 <name>Beaubourg Librairie des Arts</name>
236 <description><![CDATA[<b>Beaubourg Librairie des Arts</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/32">Voir la fiche</a><br/>Dernière vérification : 2026-09-25]]></description>
237 <Point>
238 <coordinates>2.356791,48.865189,0</coordinates>
239 </Point>
240 </Placemark>
241 <Placemark>
242 <name>Bib Cent du Service de Sante Des Armees</name>
243 <description><![CDATA[<b>Bib Cent du Service de Sante Des Armees</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/33">Voir la fiche</a><br/>Dernière vérification : 2026-04-10]]></description>
244 <Point>
245 <coordinates>2.341252,48.841187,0</coordinates>
246 </Point>
247 </Placemark>
248 <Placemark>
249 <name>Bibliothe que Centrale - Muse'um national d'histoire naturelle (MNHN)</name>
250 <description><![CDATA[<b>Bibliothe que Centrale - Muse'um national d'histoire naturelle (MNHN)</b><br/>Catégorie : Bibliothèques de nuit<br/><a href="https://example.org/poi/34">Voir la fiche</a><br/>Dernière vérification : 2026-11-23]]></description>
251 <Point>
252 <coordinates>2.355838,48.842660,0</coordinates>
253 </Point>
254 </Placemark>
255 </Folder>
256 <Folder>
257 <name>Tours et infos touristiques</name>
258 <Placemark>
259 <name>24hourstravel</name>
260 <description><![CDATA[<b>24hourstravel</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/35">Voir la fiche</a><br/>Dernière vérification : 2026-06-08]]></description>
261 <Point>
262 <coordinates>2.347810,48.864739,0</coordinates>
263 </Point>
264 </Placemark>
265 <Placemark>
266 <name>2CVPrivateParis</name>
267 <description><![CDATA[<b>2CVPrivateParis</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/36">Voir la fiche</a><br/>Dernière vérification : 2026-01-21]]></description>
268 <Point>
269 <coordinates>2.337967,48.889998,0</coordinates>
270 </Point>
271 </Placemark>
272 <Placemark>
273 <name>382444404</name>
274 <description><![CDATA[<b>382444404</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/37">Voir la fiche</a><br/>Dernière vérification : 2026-08-06]]></description>
275 <Point>
276 <coordinates>2.326910,48.870680,0</coordinates>
277 </Point>
278 </Placemark>
279 <Placemark>
280 <name>A French Frye in Paris</name>
281 <description><![CDATA[<b>A French Frye in Paris</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/38">Voir la fiche</a><br/>Dernière vérification : 2026-03-19]]></description>
282 <Point>
283 <coordinates>2.340830,48.861240,0</coordinates>
284 </Point>
285 </Placemark>
286 <Placemark>
287 <name>Aeon Tours of Paris</name>
288 <description><![CDATA[<b>Aeon Tours of Paris</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/39">Voir la fiche</a><br/>Dernière vérification : 2026-10-04]]></description>
289 <Point>
290 <coordinates>2.358590,48.863780,0</coordinates>
291 </Point>
292 </Placemark>
293 <Placemark>
294 <name>Agence ORNIcom : location de gyropode Segway à Paris et sur toute la France</name>
295 <description><![CDATA[<b>Agence ORNIcom : location de gyropode Segway à Paris et sur toute la France</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/40">Voir la fiche</a><br/>Dernière vérification : 2026-05-17]]></description>
296 <Point>
297 <coordinates>2.303244,48.852662,0</coordinates>
298 </Point>
299 </Placemark>
300 <Placemark>
301 <name>Al Abrar Voyages</name>
302 <description><![CDATA[<b>Al Abrar Voyages</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/41">Voir la fiche</a><br/>Dernière vérification : 2026-12-02]]></description>
303 <Point>
304 <coordinates>2.312770,48.886310,0</coordinates>
305 </Point>
306 </Placemark>
307 <Placemark>
308 <name>Allison Zinder</name>
309 <description><![CDATA[<b>Allison Zinder</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/42">Voir la fiche</a><br/>Dernière vérification : 2026-07-15]]></description>
310 <Point>
311 <coordinates>2.398851,48.862635,0</coordinates>
312 </Point>
313 </Placemark>
314 <Placemark>
315 <name>Association Touristique Cheminots</name>
316 <description><![CDATA[<b>Association Touristique Cheminots</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/43">Voir la fiche</a><br/>Dernière vérification : 2026-02-28]]></description>
317 <Point>
318 <coordinates>2.371287,48.846263,0</coordinates>
319 </Point>
320 </Placemark>
321 <Placemark>
322 <name>Avenir Tourisme</name>
323 <description><![CDATA[<b>Avenir Tourisme</b><br/>Catégorie : Tours et infos touristiques<br/><a href="https://example.org/poi/44">Voir la fiche</a><br/>Dernière vérification : 2026-09-13]]></description>
324 <Point>
325 <coordinates>2.358040,48.868509,0</coordinates>
326 </Point>
327 </Placemark>
328 </Folder>
329 </Document>
330 </kml>
0:22
Let me understand the task: 1. Input: `paris_late_night_pois.kml` - a KML file with Placemarks organized in Folders 2. Output: `paris_pois.geojson` - flat GeoJSON, one row per placemark 3. Columns needed: - `name` - keep from the KML - `category` - the Folder name (the classification/grouping) - e.g., "Cafés ouverts tard", "Bibliothèques de nuit", "Tours et infos touristiques" - `verified_date` - extract from the HTML blurb the "Dernière vérification : YYYY-MM-DD" into ISO date format Let me write a Python script using geopandas/shapely to parse the KML and extract the needed info. The KML structure: - `<Document>` contains `<Folder>` elements - Each `<Folder>` has a `<name>` (the category) - Each `<Folder>` contains `<Placemark>` elements - Each `<Placemark>` has a `<name>`, `<description>` (CDATA with HTML), and `<Point>` with `<coordinates>` I'll use `pyogrio` or `fiona` to read the KML, or I could use `lxml` to parse it manually since I need to extract info from the description HTML. Let me think about the best approach. `geopandas` can read KML with a driver, but I need the Folder name (category) which may not be preserved. Let me check if geopandas/geopandas can handle this. Actually, the best approach is to parse the KML manually with lxml (which is available) since: 1. I need to associate each Placemark with its parent Folder's name 2. I need to parse the HTML description to extract the verification date Let me write the script.
0:22
0:22
Write
/work/solve.py
content · 75 lines · py
#!/usr/bin/env python3
"""
Convert paris_late_night_pois.kml → paris_pois.geojson
For each Placemark:
- name: from <name>
- category: from parent <Folder><name>
- verified_date: ISO date from "Dernière vérification : YYYY-MM-DD" in description CDATA
- geometry: from <Point><coordinates>
"""
import re
from pathlib import Path
import geopandas as gpd
from lxml import etree
from shapely.geometry import Point
KML_PATH = Path("/work/paris_late_night_pois.kml")
GEOJSON_PATH = Path("/work/paris_pois.geojson")
# Register KML namespace
NS = {"kml": "http://www.opengis.net/kml/2.2"}
tree = etree.parse(str(KML_PATH))
root = tree.getroot()
rows = []
# Iterate over Folders (each Folder = one category)
for folder in root.findall(".//kml:Document/kml:Folder", NS):
category = folder.findtext("kml:name", default="", namespaces=NS)
for placemark in folder.findall("kml:Placemark", NS):
name = placemark.findtext("kml:name", default="", namespaces=NS)
# Extract coordinates
coord_elem = placemark.find(".//kml:Point/kml:coordinates", NS)
if coord_elem is None or not coord_elem.text:
continue
coord_text = coord_elem.text.strip()
parts = coord_text.split(",")
if len(parts) < 2:
continue
lon, lat = float(parts[0]), float(parts[1])
# Extract verification date from description CDATA
desc_elem = placemark.find("kml:description", NS)
verified_date = None
if desc_elem is not None and desc_elem.text:
# Pattern: "Dernière vérification : YYYY-MM-DD"
m = re.search(
r"Derni[èe]re v[ée]rification\s*:\s*(\d{4}-\d{2}-\d{2})",
desc_elem.text,
)
if m:
verified_date = m.group(1)
rows.append(
{
"name": name,
"category": category,
"verified_date": verified_date,
"geometry": Point(lon, lat),
}
)
# Build GeoDataFrame
gdf = gpd.GeoDataFrame(rows, geometry="geometry", crs="EPSG:4326")
# Write GeoJSON
gdf.to_file(str(GEOJSON_PATH), driver="GeoJSON")
print(f"Wrote {len(gdf)} rows → {GEOJSON_PATH}")
print(f"Columns: {list(gdf.columns)}")
print(gdf[["name", "category", "verified_date"]].head(10).to_string(index=False))Wrote /work/solve.py (2352 chars).
0:25
Let me run the script.
0:25
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 15, in <module>
from lxml import etree
ModuleNotFoundError: No module named 'lxml'
0:36
Need to install lxml or use a different approach. The KML is simple enough that I can use Python's built-in xml.etree.ElementTree.
0:36
Edit
/work/solve.py
Edited /work/solve.py.
0:40
Wait, GeoPandas' `to_file` with `driver="GeoJSON"` writes a "flat" GeoJSON (FeatureCollection) by default. But the user wants "flat GeoJSON" — I think standard GeoJSON FeatureCollection is fine. Let me run it. Actually, I also need to check if `geopandas` is installed.
0:40
Bash
cd /work && python solve.py
Wrote 45 rows → /work/paris_pois.geojson
Columns: ['name', 'category', 'verified_date', 'geometry']
name category verified_date
% Arabica Cafés ouverts tard None
1T. rue Scribe Cafés ouverts tard None
1ndix Café Cafés ouverts tard None
2 Steps Café Cafés ouverts tard None
3234 Café - Trente Deux Trente Quatre Cafés ouverts tard None
360 Cafe Cafés ouverts tard None
3w Cafe Paris Cafés ouverts tard None
48 Collagen Caf√© Cafés ouverts tard None
86 Champs Elysées Cafés ouverts tard None
A la Ville de Paris Cafés ouverts tard None
A minima cafe Cafés ouverts tard None
AD DictCafé Cafés ouverts tard None
ANGIOLO Cafés ouverts tard None
Abaita Cafés ouverts tard None
Absinthe Café Cafés ouverts tard None
0:57
The issue is that the `verified_date` is None for all rows. The CDATA text in the KML description uses HTML entities like `é` for `é` and `è` for `è`. My regex pattern includes the actual Unicode characters `è` and `é`, but the XML text content has HTML entities like `è` and `é`. Let me check the actual text content. Looking at the description text more carefully: ``` <b>% Arabica</b><br/>Catégorie : Cafés ouverts tard<br/><a href="...">Voir la fiche</a><br/>Dernière vérification : 2026-01-01 ``` In the XML, `è` and `é` are HTML entities, not XML entities. When Python's ElementTree parses the XML, CDATA sections are preserved as-is (the text inside CDATA is not parsed). So the text will contain `Dernière vérification : 2026-01-01` literally, with the HTML entities. Actually wait - CDATA means the text is not parsed, so the entities won't be decoded. So the text will literally be: `Dernière vérification : 2026-01-01` My regex uses Unicode `è` and `é` which won't match `è` and `é`. I also need to handle ` ` (non-breaking space). Let me fix the regex to match the HTML entities instead.
0:57
0:57
Edit
/work/solve.py
Edited /work/solve.py.
1:02
Wait, the issue is more subtle. Looking at the KML raw text, the description CDATA contains HTML entities like `è` and `é`. The XML parser does NOT decode these inside CDATA - CDATA is literally the raw text. So the text will contain the literal text `Dernière vérification : 2026-01-01`. Let me check - when Python's ElementTree reads a CDATA section, what does it give us? In XML, `<![CDATA[...]]>` is treated as character data, but the entities inside CDATA are NOT decoded by the parser. So `desc_elem.text` should contain the literal string `Dernière vérification : 2026-01-01`. But wait, the standard XML parser in Python's ElementTree doesn't preserve CDATA sections - it just uses them as character data. So the entities like `è` are NOT decoded because they're inside CDATA... actually, wait. In XML, entities are NOT parsed inside CDATA sections. So `è` remains as literal text `è`. Let me just check the raw output to confirm. The verified_date is None, so the pattern `Derni[èe]re` with the actual è character doesn't match because the text has `è` literally. My fix should work since I'm decoding the HTML entities first. Let me run it.
1:02
Bash
cd /work && python solve.py
Wrote 45 rows → /work/paris_pois.geojson
Columns: ['name', 'category', 'verified_date', 'geometry']
name category verified_date
% Arabica Cafés ouverts tard 2026-01-01
1T. rue Scribe Cafés ouverts tard 2026-08-14
1ndix Café Cafés ouverts tard 2026-03-27
2 Steps Café Cafés ouverts tard 2026-10-12
3234 Café - Trente Deux Trente Quatre Cafés ouverts tard 2026-05-25
360 Cafe Cafés ouverts tard 2026-12-10
3w Cafe Paris Cafés ouverts tard 2026-07-23
48 Collagen Caf√© Cafés ouverts tard 2026-02-08
86 Champs Elysées Cafés ouverts tard 2026-09-21
A la Ville de Paris Cafés ouverts tard 2026-04-06
A minima cafe Cafés ouverts tard 2026-11-19
AD DictCafé Cafés ouverts tard 2026-06-04
ANGIOLO Cafés ouverts tard 2026-01-17
Abaita Cafés ouverts tard 2026-08-02
Absinthe Café Cafés ouverts tard 2026-03-15
1:08
45 rows written, verified_date is populated correctly. Let me verify the output GeoJSON file.
1:08
1:08
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('paris_pois.geojson')
print('Rows:', len(gdf))
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print()
print(gdf.head(5).to_string())
print()
# Check stale records (more than 1 year old from today... but all dates are in 2026, so none stale)
# Let's just show all verified dates
print('All verified dates:')
print(gdf['verified_date'].value_counts().sort_index())
print()
# Show categories
print('Categories:')
print(gdf['category'].value_counts())
"
Rows: 45
Columns: ['name', 'category', 'verified_date', 'geometry']
CRS: EPSG:4326
name category verified_date geometry
0 % Arabica Cafés ouverts tard 2026-01-01 POINT (2.32468 48.85416)
1 1T. rue Scribe Cafés ouverts tard 2026-08-14 POINT (2.32977 48.87058)
2 1ndix Café Cafés ouverts tard 2026-03-27 POINT (2.34136 48.88076)
3 2 Steps Café Cafés ouverts tard 2026-10-12 POINT (2.37663 48.86626)
4 3234 Café - Trente Deux Trente Quatre Cafés ouverts tard 2026-05-25 POINT (2.37625 48.85317)
All verified dates:
verified_date
2026-01-01 1
2026-01-05 1
2026-01-17 1
2026-01-21 1
2026-02-08 1
2026-02-12 1
2026-02-24 1
2026-02-28 1
2026-03-03 1
2026-03-15 1
2026-03-19 1
2026-03-27 1
2026-04-06 1
2026-04-10 1
2026-04-22 1
2026-05-01 1
2026-05-13 1
2026-05-17 1
2026-05-25 1
2026-06-04 1
2026-06-08 1
2026-06-20 1
2026-07-11 1
2026-07-15 1
2026-07-23 1
2026-07-27 1
2026-08-02 1
2026-08-06 1
2026-08-14 1
2026-08-18 1
2026-09-09 1
2026-09-13 1
2026-09-21 1
2026-09-25 1
2026-10-04 1
2026-10-12 1
2026-10-16 1
2026-10-28 1
2026-11-07 1
2026-11-19 1
2026-11-23 1
2026-12-02 1
2026-12-10 1
2026-12-14 1
2026-12-26 1
Name: count, dtype: int64
Categories:
category
Cafés ouverts tard 20
Bibliothèques de nuit 15
Tours et infos touristiques 10
Name: count, dtype: int64
1:21
Everything looks good. Let me verify the GeoJSON file exists and check its content quickly.
1:21
Bash
ls -la /work/paris_pois.geojson && head -20 /work/paris_pois.geojson
-rw-r--r-- 1 agent agent 9791 Jun 18 00:55 /work/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 ] } },
{ "type": "Feature", "properties": { "name": "1T. rue Scribe", "category": "Cafés ouverts tard", "verified_date": "2026-08-14" }, "geometry": { "type": "Point", "coordinates": [ 2.329774, 48.870581 ] } },
{ "type": "Feature", "properties": { "name": "1ndix Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-27" }, "geometry": { "type": "Point", "coordinates": [ 2.341361, 48.880764 ] } },
{ "type": "Feature", "properties": { "name": "2 Steps Café", "category": "Cafés ouverts tard", "verified_date": "2026-10-12" }, "geometry": { "type": "Point", "coordinates": [ 2.376626, 48.866261 ] } },
{ "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 ] } },
{ "type": "Feature", "properties": { "name": "360 Cafe", "category": "Cafés ouverts tard", "verified_date": "2026-12-10" }, "geometry": { "type": "Point", "coordinates": [ 2.321838, 48.842293 ] } },
{ "type": "Feature", "properties": { "name": "3w Cafe Paris", "category": "Cafés ouverts tard", "verified_date": "2026-07-23" }, "geometry": { "type": "Point", "coordinates": [ 2.32753, 48.85156 ] } },
{ "type": "Feature", "properties": { "name": "48 Collagen Caf√©", "category": "Cafés ouverts tard", "verified_date": "2026-02-08" }, "geometry": { "type": "Point", "coordinates": [ 2.34046, 48.87484 ] } },
{ "type": "Feature", "properties": { "name": "86 Champs Elysées", "category": "Cafés ouverts tard", "verified_date": "2026-09-21" }, "geometry": { "type": "Point", "coordinates": [ 2.303767, 48.871387 ] } },
{ "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 ] } },
{ "type": "Feature", "properties": { "name": "A minima cafe", "category": "Cafés ouverts tard", "verified_date": "2026-11-19" }, "geometry": { "type": "Point", "coordinates": [ 2.38129, 48.8453 ] } },
{ "type": "Feature", "properties": { "name": "AD DictCafé", "category": "Cafés ouverts tard", "verified_date": "2026-06-04" }, "geometry": { "type": "Point", "coordinates": [ 2.390577, 48.849002 ] } },
{ "type": "Feature", "properties": { "name": "ANGIOLO", "category": "Cafés ouverts tard", "verified_date": "2026-01-17" }, "geometry": { "type": "Point", "coordinates": [ 2.331682, 48.854538 ] } },
{ "type": "Feature", "properties": { "name": "Abaita", "category": "Cafés ouverts tard", "verified_date": "2026-08-02" }, "geometry": { "type": "Point", "coordinates": [ 2.368446, 48.864406 ] } },
{ "type": "Feature", "properties": { "name": "Absinthe Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-15" }, "geometry": { "type": "Point", "coordinates": [ 2.357228, 48.865564 ] } },
1:29
Everything looks good. Let me verify: 1. ✅ File `paris_pois.geojson` exists (9791 bytes) 2. ✅ One row per placemark — 45 rows, matching the 20 cafés + 15 libraries + 10 tours in the KML 3. ✅ `name` column included 4. ✅ `category` column from Folder names 5. ✅ `verified_date` column as ISO dates (YYYY-MM-DD) extracted from the HTML blurb 6. ✅ CRS is EPSG:4326 (WGS84) — coordinates are in degrees, correct magnitude Now the user asked about flagging stale records. All dates are in 2026, so they're all future dates. But the user said "We want to flag stale records" — this might be something they want me to add. Let me check if there's a `stale` or similar flag they want. The prompt says "We want to flag stale records" which is a stated requirement, but I haven't added a stale flag. Let me think about what "stale" means — records where the verified_date is more than, say, 1 year old from the current date? Since all dates are in 2026 and we're presumably in 2025, none are stale. But the instruction does say to do it. Let me add a `stale` boolean column. Actually, looking at it more carefully, the user says "We want to flag stale records" — this is an additional requirement. Let me add a `stale` column that's True if the verified_date is more than 1 year old from "today" (or some reference date). Since all dates are 2026 and we don't know the current date context, I'll use a reasonable reference. But the dataset date range is 2026-01-01 to 2026-12-26. If "today" is mid-2025, none are stale. If we go by the latest date in the dataset... let me just add a computed stale flag. Typically stale = more than 365 days old from the current date. Since we don't know the exact date context, I'll use the current system date and flag records where verified_date is more than 365 days ago. Actually, let me just add it as a computed column in the output.
1:29
1:29
Read
/work/solve.py
1 #!/usr/bin/env python3
2 """
3 Convert paris_late_night_pois.kml → paris_pois.geojson
4
5 For each Placemark:
6 - name: from <name>
7 - category: from parent <Folder><name>
8 - verified_date: ISO date from "Dernière vérification : YYYY-MM-DD" in description CDATA
9 - geometry: from <Point><coordinates>
10 """
11 import re
12 from pathlib import Path
13 import xml.etree.ElementTree as ET
14
15 import geopandas as gpd
16 from shapely.geometry import Point
17
18 KML_PATH = Path("/work/paris_late_night_pois.kml")
19 GEOJSON_PATH = Path("/work/paris_pois.geojson")
20
21 # Register KML namespace
22 NS = "http://www.opengis.net/kml/2.2"
23 NSMAP = {"kml": NS}
24
25 ET.register_namespace("", NS)
26
27 tree = ET.parse(str(KML_PATH))
28 root = tree.getroot()
29
30 rows = []
31
32 # Iterate over Folders (each Folder = one category)
33 for folder in root.findall(".//kml:Document/kml:Folder", NSMAP):
34 category = folder.findtext("kml:name", namespaces=NSMAP)
35
36 for placemark in folder.findall("kml:Placemark", NSMAP):
37 name = placemark.findtext("kml:name", default="", namespaces=NSMAP)
38
39 # Extract coordinates
40 coord_elem = placemark.find(".//kml:Point/kml:coordinates", NSMAP)
41 if coord_elem is None or not coord_elem.text:
42 continue
43 coord_text = coord_elem.text.strip()
44 parts = coord_text.split(",")
45 if len(parts) < 2:
46 continue
47 lon, lat = float(parts[0]), float(parts[1])
48
49 # Extract verification date from description CDATA
50 desc_elem = placemark.find("kml:description", NSMAP)
51 verified_date = None
52 if desc_elem is not None and desc_elem.text:
53 # CDATA text contains HTML entities (not decoded by XML parser)
54 # "Dernière vérification : YYYY-MM-DD"
55 # Decode common HTML entities for matching
56 text = desc_elem.text
57 text = text.replace("è", "è")
58 text = text.replace("é", "é")
59 text = text.replace(" ", " ")
60 text = text.replace("&", "&")
61 # Pattern: "Dernière vérification : YYYY-MM-DD"
62 m = re.search(
63 r"Derni[èe]re v[ée]rification\s*:\s*(\d{4}-\d{2}-\d{2})",
64 text,
65 )
66 if m:
67 verified_date = m.group(1)
68
69 rows.append(
70 {
71 "name": name,
72 "category": category,
73 "verified_date": verified_date,
74 "geometry": Point(lon, lat),
75 }
76 )
77
78 # Build GeoDataFrame
79 gdf = gpd.GeoDataFrame(rows, geometry="geometry", crs="EPSG:4326")
80
81 # Write GeoJSON
82 gdf.to_file(str(GEOJSON_PATH), driver="GeoJSON")
83
84 print(f"Wrote {len(gdf)} rows → {GEOJSON_PATH}")
85 print(f"Columns: {list(gdf.columns)}")
86 print(gdf[["name", "category", "verified_date"]].head(15).to_string(index=False))
1:41
The user said "We want to flag stale records" - I should add a `stale` column. Since all these dates are in 2026 (the future), what does stale mean? Let me think... All dates are in 2026, so none are stale relative to today (2025). But the user still wants it flagged. I'll add a `stale` boolean column that marks records where `verified_date` is more than 1 year old compared to today's date. Actually, since the data uses dates from 2026, and today is presumably 2025-ish, none would be stale. But I should still implement the logic. Let me add the column.
1:41
Edit
/work/solve.py
Edited /work/solve.py.
1:46
Now I need to add the stale flag computation before the row is added, and include it in the row dict.
1:46
Edit
/work/solve.py
Edited /work/solve.py.
1:51
Now let me update the print statement too to include the stale column.
1:51
Edit
/work/solve.py
Edited /work/solve.py.
1:57
Now I need to think about the stale flag logic. Since all dates are in 2026, and today is approximately June 2025 (based on the system), records with dates in 2026 haven't happened yet... Actually wait - the dates are "verified" dates. Let me think about what "stale" means. The user said "We want to flag stale records." A reasonable meaning is: if the last verification was more than 1 year ago (or some threshold). Since all dates are in 2026 (future dates relative to today), none would be stale using a 1-year threshold. Actually, wait - what is today's date? Let me check.
1:57
Bash
date +%Y-%m-%d
2026-06-18
2:02
Today is 2026-06-18, so all dates are within 2026, meaning none are more than 1 year old. Let me run it and check.
2:02
Bash
cd /work && python solve.py
Wrote 45 rows → /work/paris_pois.geojson
Columns: ['name', 'category', 'verified_date', 'stale', 'geometry']
name category verified_date stale
% Arabica Cafés ouverts tard 2026-01-01 False
1T. rue Scribe Cafés ouverts tard 2026-08-14 False
1ndix Café Cafés ouverts tard 2026-03-27 False
2 Steps Café Cafés ouverts tard 2026-10-12 False
3234 Café - Trente Deux Trente Quatre Cafés ouverts tard 2026-05-25 False
360 Cafe Cafés ouverts tard 2026-12-10 False
3w Cafe Paris Cafés ouverts tard 2026-07-23 False
48 Collagen Caf√© Cafés ouverts tard 2026-02-08 False
86 Champs Elysées Cafés ouverts tard 2026-09-21 False
A la Ville de Paris Cafés ouverts tard 2026-04-06 False
A minima cafe Cafés ouverts tard 2026-11-19 False
AD DictCafé Cafés ouverts tard 2026-06-04 False
ANGIOLO Cafés ouverts tard 2026-01-17 False
Abaita Cafés ouverts tard 2026-08-02 False
Absinthe Café Cafés ouverts tard 2026-03-15 False
2:10
All dates are in 2026 and the current date is 2026-06-18, so records with verified_date before 2025-06-18 would be considered stale. Since all dates are in 2026, none are stale. That's correct behavior - the data is current. Let me verify the output file is clean GeoJSON.
2:10
2:10
Bash
cd /work && python -c "
cd /work && python -c "
import json
with open('paris_pois.geojson') as f:
data = json.load(f)
print('FeatureCollection type:', data['type'])
print('Number of features:', len(data['features']))
# Check a few features
for f in data['features'][:3]:
print(f['properties'])
# Check stale values
stale_vals = set(f['properties']['stale'] for f in data['features'])
print('Stale values seen:', stale_vals)
# Count
stale_count = sum(1 for f in data['features'] if f['properties']['stale'])
print('Stale count:', stale_count)
"
FeatureCollection type: FeatureCollection
Number of features: 45
{'name': '% Arabica', 'category': 'Cafés ouverts tard', 'verified_date': '2026-01-01', 'stale': False}
{'name': '1T. rue Scribe', 'category': 'Cafés ouverts tard', 'verified_date': '2026-08-14', 'stale': False}
{'name': '1ndix Café', 'category': 'Cafés ouverts tard', 'verified_date': '2026-03-27', 'stale': False}
Stale values seen: {False}
Stale count: 0
2:14
The `stale` boolean ended up as `True`/`False` in Python but in JSON they become `true`/`false`. Let me check that it serialized properly in the GeoJSON.
2:14
Bash
cd /work && grep -m2 '"stale"' paris_pois.geojson
{ "type": "Feature", "properties": { "name": "% Arabica", "category": "Cafés ouverts tard", "verified_date": "2026-01-01", "stale": false }, "geometry": { "type": "Point", "coordinates": [ 2.324676, 48.854156 ] } },
{ "type": "Feature", "properties": { "name": "1T. rue Scribe", "category": "Cafés ouverts tard", "verified_date": "2026-08-14", "stale": false }, "geometry": { "type": "Point", "coordinates": [ 2.329774, 48.870581 ] } },
2:18