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"
}
0:11
Let me start by exploring what `paris_late_night_pois` is - it seems to be the input file that was provided. Let me look at the working directory first.
0:11
Bash
ls /work/
paris_late_night_pois.kml
0:16
Let me first look at the KML file to understand its structure.
0:16
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>
0:24
Let me analyze the KML structure:
1. It has a `<Document>` with a `<Folder>` for each category:
- "Cafés ouverts tard"
- "Bibliothèques de nuit"
- "Tours et infos touristiques"
2. Each `<Placemark>` has:
- `<name>` - the name of the place
- `<description>` with CDATA containing HTML with:
- `<b>name</b>` (same as name)
- `Catégorie : <category>` - the category/grouping
- A link
- `Dernière vérification : <date>` - the last verified date
- `<Point>` with `<coordinates>` (lon,lat,alt)
I need to:
1. Parse the KML file
2. For each Placemark, extract:
- `name` - from the `<name>` element
- `category` - from the Folder name that contains it
- `verified_date` - from the description blurb (extract date after "Dernière vérification : ")
- `geometry` - point from coordinates
3. Convert to flat GeoJSON (one row per placemark)
4. Output to `paris_pois.geojson`
Let me write a Python script using `pyogrio` and `shapely` and `os` (for HTML parsing) to do this. Actually, `pyogrio` can read KML files, but the challenge is extracting the verified_date from the description field.
Actually, let me think about the best approach. I can use `pyogrio` or `fiona` to read the KML, get the description and folder info, then use regex or parsing to extract the date.
Let me write the script.
Actually, `pyogrio` can read KML files into a GeoDataFrame. But the folder information (category) might be preserved in a column. Let me check - in KML, pyogrio typically flattens the structure. The category might be in a "Folder" or similar field.
Let me write a script using `pyogrio` to read it first and see what columns we get.
Actually, let me just write a comprehensive script that uses `lxml` or `xml.etree.ElementTree` to parse the KML properly, since I need to handle the Folder structure for the category, and extract the date from the HTML description.
Let me use `xml.etree.ElementTree` with proper namespace handling.
0:24
Bash
python3 -c "
python3 -c "
import pyogrio
gdf = pyogrio.read_dataframe('/work/paris_late_night_pois.kml')
print(gdf.columns.tolist())
print(gdf.head(2))
"
['id', 'Name', 'description', 'timestamp', 'begin', 'end', 'altitudeMode', 'tessellate', 'extrude', 'visibility', 'drawOrder', 'icon', 'geometry']
id Name ... icon geometry
0 None % Arabica ... None POINT Z (2.32468 48.85416 0)
1 None 1T. rue Scribe ... None POINT Z (2.32977 48.87058 0)
[2 rows x 13 columns]
/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(
0:33
So pyogrio can read the KML. It warns about multiple layers - the Folder names become layers. Let me read each layer separately and combine them. The description field contains the HTML blurb with: 1. `<b>Name</b><br/>Catégorie : Category<br/><a href="...">Voir la fiche</a><br/>Dernière vérification : YYYY-MM-DD` I need to: 1. Parse out the category (from "Catégorie : ...") - this maps from the Folder/grouping to a `category` column 2. Parse out the "Dernière vérification : YYYY-MM-DD" date 3. Keep the `name` from the Placemark <name> element 4. Convert to flat GeoJSON (drops the Z from coordinates) Let me write a script.
0:33
Bash
python3 -c "
python3 -c "
import pyogrio
# Check available layer names
print(pyogrio.list_layers('/work/paris_late_night_pois.kml'))
"
[['Cafés ouverts tard' 'Unknown'] ['Bibliothèques de nuit' 'Unknown'] ['Tours et infos touristiques' 'Unknown']]
0:43
The KML has multiple layers (Folders). I need to read each one, add a category column from the folder name, and parse the HTML description to extract the "Dernière vérification" date as an ISO date. Let me write a Python script that: 1. Reads each layer separately 2. Renames columns 3. Parses the description HTML to extract the "Dernière vérification" date 4. Combines all into one GeoDataFrame 5. Outputs to GeoJSON The user wants: - `name` column (the placemark name) - `category` column (the folder/grouping name) - `verified_date` column (ISO date from the HTML description) - One row per placemark Let me also think about flagging stale records. The user says "We want to flag stale records." I should add a `stale` boolean column or similar — records where the verified date is more than, say, 1 year old from today. Let me include that. Actually, let me re-read: "We want to flag stale records." I'll add a boolean column `is_stale` that is True if the verified_date is older than 1 year from the current date. Let me write the script.
0:43
Write
/work/solve.py
content · 60 lines · py
import geopandas as gpd
import pyogrio
import pandas as pd
import re
from datetime import datetime, timezone, timedelta
KML_PATH = "/work/paris_late_night_pois.kml"
OUT_PATH = "/work/paris_pois.geojson"
# Read all layers from the KML
layers_info = pyogrio.list_layers(KML_PATH)
layer_names = [row[0] for row in layers_info]
frames = []
for layer in layer_names:
gdf = pyogrio.read_dataframe(KML_PATH, layer=layer)
# Drop columns that are all-None and not needed
gdf = gdf.drop(columns=[c for c in gdf.columns if gdf[c].isna().all() and c not in ("Name", "description", "geometry")], errors="ignore")
gdf["category"] = layer
frames.append(gdf)
df = pd.concat(frames, ignore_index=True)
# Rename "Name" -> "name"
df = df.rename(columns={"Name": "name"})
# Parse the verified date from the description HTML
# Pattern: Dernière vérification : YYYY-MM-DD
def extract_verified(desc):
if pd.isna(desc) or desc is None:
return None
m = re.search(r"Derni[èe]re v[ée]rification\s*:\s*(\d{4}-\d{2}-\d{2})", desc)
if m:
return m.group(1)
return None
df["verified_date"] = df["description"].apply(extract_verified)
# Flag stale records (verified > 1 year ago from today)
today = datetime.now(timezone.utc).date()
one_year_ago = today - timedelta(days=365)
def is_stale(vd):
if pd.isna(vd) or vd is None:
return True # no verification date = stale
try:
d = datetime.strptime(vd, "%Y-%m-%d").date()
return d < one_year_ago
except (ValueError, TypeError):
return True
df["is_stale"] = df["verified_date"].apply(is_stale)
# Keep only requested columns + geometry
out = gpd.GeoDataFrame(df[["name", "category", "verified_date", "is_stale", "geometry"]], geometry="geometry", crs="EPSG:4326")
out.to_file(OUT_PATH, driver="GeoJSON")
print(f"Written {len(out)} rows to {OUT_PATH}")
print(out.head(3))Wrote /work/solve.py (1890 chars).
0:48
Wait, let me re-read the instructions. The user said "include the source's classification/grouping as a `category` column". The KML uses `<Folder>` elements with names like "Cafés ouverts tard", "Bibliothèques de nuit", "Tours et infos touristiques". These are the layer names. So I'm using the layer name as the category. That's right. Also the user said "pull each entry's 'last verified' date out of the HTML blurb into a `verified_date` column as an ISO date." The ISO date format is YYYY-MM-DD, which the dates already are in. Good. "We want to flag stale records." - I added an `is_stale` boolean column based on verification > 1 year ago. Let me run the script and see if it works.
0:48
Bash
cd /work && python solve.py
Written 45 rows to /work/paris_pois.geojson
name category ... is_stale geometry
0 % Arabica Cafés ouverts tard ... True POINT Z (2.32468 48.85416 0)
1 1T. rue Scribe Cafés ouverts tard ... True POINT Z (2.32977 48.87058 0)
2 1ndix Café Cafés ouverts tard ... True POINT Z (2.34136 48.88076 0)
[3 rows x 5 columns]
0:57
Let me verify the output looks correct. Let me check a few records to make sure the dates are parsed correctly and the stale flag works. Wait - the dates in the data are all in 2026. If today is... let me check what the system considers "today." The script used `datetime.now(timezone.utc).date()`. Let me check the current date.
0:57
Bash
date -u && python3 -c "
date -u && python3 -c "
import json
with open('/work/paris_pois.geojson') as f:
data = json.load(f)
feats = data['features']
for f in feats[:5]:
print(f['properties'])
"
Tue Jun 16 17:59:34 UTC 2026
{'name': '% Arabica', 'category': 'Cafés ouverts tard', 'verified_date': None, 'is_stale': True}
{'name': '1T. rue Scribe', 'category': 'Cafés ouverts tard', 'verified_date': None, 'is_stale': True}
{'name': '1ndix Café', 'category': 'Cafés ouverts tard', 'verified_date': None, 'is_stale': True}
{'name': '2 Steps Café', 'category': 'Cafés ouverts tard', 'verified_date': None, 'is_stale': True}
{'name': '3234 Café - Trente Deux Trente Quatre', 'category': 'Cafés ouverts tard', 'verified_date': None, 'is_stale': True}
1:05
The verified_date is coming back as None for all records. The regex must not be matching. Let me check the description content more carefully. Looking at line 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> ``` The HTML entities like `è` represent è, and `é` represents é. But in the description field as read by pyogrio, these might be decoded or not. Let me check what the actual description text looks like.
1:05
Bash
python3 -c "
python3 -c "
import pyogrio
gdf = pyogrio.read_dataframe('/work/paris_late_night_pois.kml', layer='Cafés ouverts tard')
print(repr(gdf['description'].iloc[0]))
print('---')
print(repr(gdf['description'].iloc[3]))
"
'<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' --- '<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'
1:13
The issue is clear now. The `description` field contains HTML entities like `è` (è) and `é` (é), so my regex pattern `Derni[èe]re v[ée]rification` doesn't match because the actual text has `Dernière vérification`. Let me fix the regex to handle the HTML-encoded text.
1:13
1:13
Read
/work/solve.py
1 import geopandas as gpd
2 import pyogrio
3 import pandas as pd
4 import re
5 from datetime import datetime, timezone, timedelta
6
7 KML_PATH = "/work/paris_late_night_pois.kml"
8 OUT_PATH = "/work/paris_pois.geojson"
9
10 # Read all layers from the KML
11 layers_info = pyogrio.list_layers(KML_PATH)
12 layer_names = [row[0] for row in layers_info]
13
14 frames = []
15 for layer in layer_names:
16 gdf = pyogrio.read_dataframe(KML_PATH, layer=layer)
17 # Drop columns that are all-None and not needed
18 gdf = gdf.drop(columns=[c for c in gdf.columns if gdf[c].isna().all() and c not in ("Name", "description", "geometry")], errors="ignore")
19
20 gdf["category"] = layer
21 frames.append(gdf)
22
23 df = pd.concat(frames, ignore_index=True)
24
25 # Rename "Name" -> "name"
26 df = df.rename(columns={"Name": "name"})
27
28 # Parse the verified date from the description HTML
29 # Pattern: Dernière vérification : YYYY-MM-DD
30 def extract_verified(desc):
31 if pd.isna(desc) or desc is None:
32 return None
33 m = re.search(r"Derni[èe]re v[ée]rification\s*:\s*(\d{4}-\d{2}-\d{2})", desc)
34 if m:
35 return m.group(1)
36 return None
37
38 df["verified_date"] = df["description"].apply(extract_verified)
39
40 # Flag stale records (verified > 1 year ago from today)
41 today = datetime.now(timezone.utc).date()
42 one_year_ago = today - timedelta(days=365)
43
44 def is_stale(vd):
45 if pd.isna(vd) or vd is None:
46 return True # no verification date = stale
47 try:
48 d = datetime.strptime(vd, "%Y-%m-%d").date()
49 return d < one_year_ago
50 except (ValueError, TypeError):
51 return True
52
53 df["is_stale"] = df["verified_date"].apply(is_stale)
54
55 # Keep only requested columns + geometry
56 out = gpd.GeoDataFrame(df[["name", "category", "verified_date", "is_stale", "geometry"]], geometry="geometry", crs="EPSG:4326")
57
58 out.to_file(OUT_PATH, driver="GeoJSON")
59 print(f"Written {len(out)} rows to {OUT_PATH}")
60 print(out.head(3))
1:22
The issue is that pyogrio returns the description with HTML entities still encoded (like `è` and `é`). My regex uses the Unicode characters directly. I need to decode the HTML entities first, or match against the entity-encoded versions. Let me look at the actual string more carefully. The string from pyogrio contains `Dernière vérification`. So I need to either: 1. Decode HTML entities before matching, or 2. Match the entity patterns. Let me use `html.unescape` from the standard library.
1:22
Edit
/work/solve.py
Edited /work/solve.py.
1:28
Now I need to also decode the HTML entities before parsing. Let me update the extract_verified function to unescape HTML first.
1:28
Edit
/work/solve.py
Edited /work/solve.py.
1:33
Now also, the `is_stale` logic — the current date is June 16, 2026. So one year ago is June 16, 2025. Let me check: dates in the data range from 2026-01-01 to 2026-12-26. So none are before June 16, 2025 — they're all after that. So none should be stale (except maybe null ones). But the user didn't specify what "stale" means exactly — they just said "flag stale records". I'll use a reasonable definition of > 1 year old. In this case, since all dates are in 2026, none are stale (verified_date exists and is within 1 year). The `is_stale` will be False for records with dates. That seems fine. Let me re-run.
1:33
Bash
cd /work && python solve.py
Written 45 rows to /work/paris_pois.geojson
name category ... is_stale geometry
0 % Arabica Cafés ouverts tard ... False POINT Z (2.32468 48.85416 0)
1 1T. rue Scribe Cafés ouverts tard ... False POINT Z (2.32977 48.87058 0)
2 1ndix Café Cafés ouverts tard ... False POINT Z (2.34136 48.88076 0)
[3 rows x 5 columns]
1:40
Now the `verified_date` is being extracted. Let me verify a few rows to make sure it's all correct.
1:40
Bash
python3 -c "
python3 -c "
import json
with open('/work/paris_pois.geojson') as f:
data = json.load(f)
feats = data['features']
# Check a sampling of properties across categories
for f in feats:
p = f['properties']
if p['name'] in ('% Arabica', 'ANGIOLO', 'Aeon Tours of Paris', 'BNF', 'After Place'):
print(p)
"
{'name': '% Arabica', 'category': 'Cafés ouverts tard', 'verified_date': '2026-01-01', 'is_stale': False}
{'name': 'ANGIOLO', 'category': 'Cafés ouverts tard', 'verified_date': '2026-01-17', 'is_stale': False}
{'name': 'After Place', 'category': 'Cafés ouverts tard', 'verified_date': '2026-12-26', 'is_stale': False}
{'name': 'After Place', 'category': 'Cafés ouverts tard', 'verified_date': '2026-07-11', 'is_stale': False}
{'name': 'After Place', 'category': 'Cafés ouverts tard', 'verified_date': '2026-02-24', 'is_stale': False}
{'name': 'BNF', 'category': 'Bibliothèques de nuit', 'verified_date': '2026-12-14', 'is_stale': False}
{'name': 'Aeon Tours of Paris', 'category': 'Tours et infos touristiques', 'verified_date': '2026-10-04', 'is_stale': False}
1:50
Let me check the CRS - the coordinates are in degrees (lon/lat), so EPSG:4326 is correct. Let me also verify the output file exists and has the right structure. The data looks good now. All dates are in 2026 and today is June 16, 2026, so nothing is more than a year old, hence no records flagged as stale. Let me verify the file is valid GeoJSON.
1:50
1:50
Bash
python3 -c "
python3 -c "
import geopandas as gpd
import json
gdf = gpd.read_file('/work/paris_pois.geojson')
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('Total stale:', gdf['is_stale'].sum())
print()
# Verify valid GeoJSON
with open('/work/paris_pois.geojson') as f:
gj = json.load(f)
print('GeoJSON type:', gj['type'])
print('Num features:', len(gj['features']))
print('Coord sample:', gj['features'][0]['geometry']['coordinates'])
"
CRS: EPSG:4326 Shape: (45, 5) Columns: ['name', 'category', 'verified_date', 'is_stale', 'geometry'] Total stale: 0 GeoJSON type: FeatureCollection Num features: 45 Coord sample: [2.324676, 48.854156, 0.0]
2:00
Let me check what the current date is — it's "Tue Jun 16 17:59:34 UTC 2026" as shown earlier. So today is 2026-06-16, and one year ago would be 2025-06-16. All dates in the data are in 2026 (all verified in 2026), so none should be stale. That's correct. All results look good: 1. **45 rows** (all placemarks from all 3 folders) 2. **Columns**: `name`, `category`, `verified_date`, `is_stale`, `geometry` 3. **CRS**: EPSG:4326 (correct for GeoJSON, coordinates in lon/lat) 4. **verified_date**: Properly extracted ISO dates (e.g. `2026-01-01`) 5. **category**: The folder/grouping names are used as categories 6. **is_stale**: All False since all dates are in 2026 and today is 2026-06-16 Output file: `/work/paris_pois.geojson` — done.
2:00
2:00
Bash
ls -la /work/paris_pois.geojson
-rw-r--r-- 1 agent agent 10871 Jun 16 18:00 /work/paris_pois.geojson
2:48
All good. Let me summarize what was done. The task asked for: 1. ✅ Convert KML to `paris_pois.geojson` — one row per placemark ✅ (45 features) 2. ✅ Keep `name` column ✅ 3. ✅ Include source classification/grouping as a `category` column ✅ (derived from Folder names: "Cafés ouverts tard", "Bibliothèques de nuit", "Tours et infos touristiques") 4. ✅ Pull each entry's 'last verified' date from HTML blurb into `verified_date` as ISO date ✅ 5. ✅ Flag stale records with `is_stale` column ✅ (all dates were in 2026 and today is 2026-06-16, so none are older than 1 year — correct behavior) The solution: - Used pyogrio to read all KML layers - Extracted the category from the layer/Folder name - Parsed the HTML description by unescaping HTML entities first, then regex matched the date - Flagged records where `verified_date` is more than 1 year old (or missing) as stale
2:48