spa-l1-vienna-pip-count
Map (reference ← swipe → agent)
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I'm pulling together a coverage diagnostic for next year's air-quality budget round, and the committee wants to spot under-monitored areas at a glance. Can you take `stations.geojson` and `districts.geojson` and write `stations_per_district.csv` listing every Bezirk with its station count?
Please keep it as a plain CSV with no geometry column, one row per Bezirk. Use three columns with these exact names: `district_code` (integer-valued, the Bezirk number, not other identifier columns), `district_name` (the human-readable German Bezirk name from the districts layer), and `station_count` (integer). Sort rows by `district_code`.
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
districts.geojson stations.geojson
0:13
Write
/work/solve.py
content · 31 lines · py
import geopandas as gpd
import pandas as pd
# Load the data
districts = gpd.read_file('districts.geojson')
stations = gpd.read_file('stations.geojson')
# Inspect columns to find correct names
print("Districts columns:", districts.columns.tolist())
print("Districts head:\n", districts.head())
print("\nStations columns:", stations.columns.tolist())
print("Stations head:\n", stations.head())
# Assuming spatial join is needed if they are linked by location,
# but the prompt implies counting stations per district.
# Let's do a spatial join.
# Ensure both are in the same CRS
if districts.crs != stations.crs:
stations = stations.to_crs(districts.crs)
# Spatial join: which station is in which district
# We want to count stations per district.
joined = gpd.sjoin(stations, districts, how='left', predicate='within')
# Group by district columns
# We need 'district_code' (integer) and 'district_name' (German name)
# Based on the prompt, I need to identify which columns these are from the inspect output.
# Let's wait to see the output of the inspect.
Wrote /work/solve.py (1065 chars).
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Bash
python /work/solve.py
Districts columns: ['district_code', 'district_name', 'osm_relation_id', 'geometry']
Districts head:
district_code ... geometry
0 1 ... POLYGON ((626064.247 482275.213, 626169.413 48...
1 2 ... POLYGON ((627606.815 486384.807, 627747.697 48...
2 3 ... POLYGON ((630274.402 480927.246, 630260.17 480...
3 4 ... POLYGON ((625107.183 481597.703, 625103.736 48...
4 5 ... POLYGON ((624887.555 481892.415, 624901.681 48...
[5 rows x 4 columns]
Stations columns: ['station_id', 'name', 'geometry']
Stations head:
station_id name geometry
0 252548493 Neubaugürtel POINT (623360.378 482029.348)
1 441581664 Operngasse POINT (625453.509 482337.109)
2 1328552671 Argentinierstraße POINT (625963.743 481655.536)
3 1569815300 Wien/Stammersdorf POINT (627853.106 494117.279)
4 1569941334 Wien-Jubiläumswarte POINT (617815.807 484317.253)
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Edit
/work/solve.py
old_string not found in /work/solve.py
0:53
Write
/work/solve.py
content · 38 lines · py
import geopandas as gpd
import pandas as pd
# Load the data
districts = gpd.read_file('districts.geojson')
stations = gpd.read_file('stations.geojson')
# Ensure both are in the same CRS
if districts.crs != stations.crs:
stations = stations.to_crs(districts.crs)
# Spatial join: which station is in which district
# We use a right join on districts to ensure all districts are represented in the result.
joined = gpd.sjoin(stations, districts, how='right', predicate='within')
# Group by district_code and district_name and count station_id
# 'station_id' is a column in the stations layer.
# The join will result in NaN for 'station_id' if no station is found in the district.
result = joined.groupby(['district_code', 'district_name'])['station_id'].count().reset_index()
# Rename the station_id count column to station_count
result.rename(columns={'station_id': 'station_count'}, inplace=True)
# Ensure the types are correct
result['district_code'] = result['district_code'].astype(int)
result['station_count'] = result['station_count'].astype(int)
# Sort by district_code
result.sort_values('district_code', inplace=True)
# Select only the required columns
result = result[['district_code', 'district_name', 'station_count']]
# Save to CSV
result.to_csv('stations_per_district.csv', index=False)
print("Successfully created stations_per_district.csv")
print(result)
Wrote /work/solve.py (1382 chars).
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Bash
python /work/solve.py
Successfully created stations_per_district.csv
district_code district_name station_count
0 1 Innere Stadt 6
1 2 Leopoldstadt 3
2 3 Landstraße 1
3 4 Wieden 2
4 5 Margareten 1
5 6 Mariahilf 0
6 7 Neubau 1
7 8 Josefstadt 0
8 9 Alsergrund 1
9 10 Favoriten 4
10 11 Simmering 0
11 12 Meidling 1
12 13 Hietzing 3
13 14 Penzing 1
14 15 Rudolfsheim-Fünfhaus 1
15 16 Ottakring 4
16 17 Hernals 0
17 18 Währing 1
18 19 Döbling 6
19 20 Brigittenau 1
20 21 Floridsdorf 3
21 22 Donaustadt 7
22 23 Liesing 2
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Bash
head -n 5 stations_per_district.csv
district_code,district_name,station_count 1,Innere Stadt,6 2,Leopoldstadt,3 3,Landstraße,1 4,Wieden,2
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