fio-l1-nyc-csvwkt-addresses
Map (reference ← swipe → agent)
0:00
The vendor SQL export `nyc_addresses` needs proper GeoParquet so `WHERE recorded_at > '2024-01-01'` and `SUM(unit_count)` work without CASTs. Convert it to `addresses.geoparquet`: Point geometry in EPSG:4326, `recorded_at` as `timestamp[us]`, `unit_count` as `int32`, all other columns as string.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by reading the input file to understand its structure.
0:03
Bash
ls /work/
nyc_addresses.csv
0:07
Let me first look at the CSV file to understand its structure.
0:07
Bash
head -20 /work/nyc_addresses.csv
"id","country","postcode","street","number","unit","postal_city","recorded_at","unit_count","geometry_wkt" "0005eda0-2540-4e36-bc81-dfb52e323d99","US","10002","ALLEN ST","37","1/2","","2024-01-01T00:00:00Z","0","POINT (-73.9922338 40.716163)" "00587e13-6253-485d-9258-e47d43af7559","US","10038","MADISON ST","45","","","2024-01-12T07:13:17Z","0","POINT (-73.9983308 40.71204)" "0070f126-d36a-43b3-b767-a8fbb7156602","US","10038","PECK SLIP","44","","","2024-01-23T14:26:34Z","0","POINT (-74.0015005 40.7073293)" "00a8fc38-5d9a-42da-94a9-7d2f4101f82c","US","11201","FRONT ST","11","","","2024-02-03T21:39:51Z","0","POINT (-73.9928578 40.702681)" "00f815af-51f6-44c2-b0d6-ee23092d14b2","US","10280","ALBANY ST","340","","","2024-02-14T04:52:08Z","1","POINT (-74.0175967 40.7102422)" "014806e3-0deb-4be3-abd5-2be10dedf40c","US","10013","CHURCH ST","299","","","2024-02-25T11:05:25Z","0","POINT (-74.0043074 40.7193005)" "018c3b29-04d1-4890-a12b-8098f6f0c7de","US","10013","WALKER ST","37","","","2024-03-07T18:18:42Z","0","POINT (-74.0040214 40.7192277)" "01cbed19-2940-4b4a-9d90-6f0be7f1b604","US","10012","MOTT ST","184","","","2024-03-18T01:31:59Z","0","POINT (-73.9957225 40.7207067)" "01fadb51-074c-4050-a670-4a615337d972","US","10002","E BROADWAY","53","","","2024-03-29T08:44:16Z","2","POINT (-73.9955533 40.7135006)" "02338589-3333-46a3-8adb-84b4ab20dd49","US","10007","WORLD TRADE CENTER","1","","","2024-04-09T15:57:33Z","0","POINT (-74.0130135 40.7132465)" "028bc5b7-7987-49b6-b764-be1f6f81f309","US","10012","THOMPSON ST","68","","","2024-04-20T22:10:50Z","0","POINT (-74.0030133 40.7242823)" "02ad9ccb-a580-45ef-8488-d3b35b58f3e4","US","10014","AVE OF THE AMERICAS","260","","","2024-05-01T05:23:07Z","0","POINT (-74.0023323 40.7289513)" "02eb28d4-4e3d-4eb0-a72d-b3795bdb5a5b","US","10012","WOOSTER ST","139","","","2024-05-12T12:36:24Z","3","POINT (-73.9998396 40.7258502)" "031f3e1d-7b14-4f57-8b3f-50d3fb2242e7","US","10013","DESBROSSES ST","2","","","2024-05-23T19:49:41Z","0","POINT (-74.0084458 40.7234422)" "037716a1-f77e-4fda-bb9f-5861994e15ef","US","10013","MOTT ST","85","","","2024-06-03T02:02:58Z","0","POINT (-73.9979789 40.7166695)" "03b4681c-0c78-4483-9d20-714c8315ea11","US","10013","BAXTER ST","120","","","2024-06-14T09:15:15Z","0","POINT (-73.9991465 40.7180102)" "0400947d-6748-449c-9ab9-177fa96ccaaf","US","10013","GRAND ST","154","","","2024-06-25T16:28:32Z","4","POINT (-73.9988115 40.7200596)" "04382316-a3fe-4442-bef1-b0e1b64f0502","US","11201","MIDDAGH ST","12","","","2024-07-06T23:41:49Z","0","POINT (-73.9946485 40.7007669)" "04858323-61a9-4293-a2bf-14d3e8f97359","US","10012","MAC DOUGAL ST","48","","","2024-07-17T06:54:06Z","0","POINT (-74.0026039 40.7273331)"
0:07
Bash
wc -l /work/nyc_addresses.csv
1057 /work/nyc_addresses.csv
0:13
I have a CSV file with NYC addresses. Let me understand the structure: Columns: id, country, postcode, street, number, unit, postal_city, recorded_at, unit_count, geometry_wkt The geometry is in WKT format with POINT coordinates in EPSG:4326 (lat/lon). I need to convert this to a GeoParquet file with: - Point geometry in EPSG:4326 - `recorded_at` as `timestamp[us]` (timestamp with microsecond precision) - `unit_count` as `int32` - All other columns as string Let me write a Python script using geopandas to do this conversion.
0:13
Write
/work/solve.py
content · 42 lines · py
import geopandas as gpd
import pandas as pd
from shapely import wkt
# Read the CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# Create GeoDataFrame with EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')
# Drop the raw WKT column (no longer needed)
gdf = gdf.drop(columns=['geometry_wkt'])
# Cast recorded_at to timestamp[us] (microsecond precision)
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
# Cast unit_count to int32
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# Ensure all other columns are string
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
for col in string_cols:
# Fill NaN with empty string before converting to avoid "nan" strings
gdf[col] = gdf[col].fillna('').astype('string')
# Write to GeoParquet
gdf.to_parquet('/work/addresses.geoparquet', index=False)
print("Conversion complete. File written to /work/addresses.geoparquet")
# Quick validation
check = gpd.read_parquet('/work/addresses.geoparquet')
print(f"Rows: {len(check)}")
print(f"CRS: {check.crs}")
print(f"Geometry type: {check.geometry.type.iloc[0]}")
print(f"recorded_at dtype: {check['recorded_at'].dtype}")
print(f"unit_count dtype: {check['unit_count'].dtype}")
print(f"All other columns are string: {all(check[c].dtype.name == 'string' for c in string_cols)}")
print(check.dtypes)Wrote /work/solve.py (1472 chars).
0:16
Let me run the script now.
0:16
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 18, in <module>
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6541, in astype
new_data = self._mgr.astype(dtype=dtype, errors=errors)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/managers.py", line 614, in astype
return self.apply("astype", dtype=dtype, errors=errors)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/managers.py", line 445, in apply
applied = getattr(b, f)(**kwargs)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/blocks.py", line 607, in astype
new_values = astype_array_safe(values, dtype, errors=errors)
File "/usr/local/lib/python3.14/site-packages/pandas/core/dtypes/astype.py", line 240, in astype_array_safe
new_values = astype_array(values, dtype, copy=copy)
File "/usr/local/lib/python3.14/site-packages/pandas/core/dtypes/astype.py", line 182, in astype_array
values = values.astype(dtype, copy=copy)
File "/usr/local/lib/python3.14/site-packages/pandas/core/arrays/datetimes.py", line 745, in astype
raise TypeError(
...<3 lines>...
)
TypeError: Cannot use .astype to convert from timezone-aware dtype to timezone-naive dtype. Use obj.tz_localize(None) or obj.tz_convert('UTC').tz_localize(None) instead.
0:21
The timestamps are timezone-aware (UTC). I need to remove timezone info first, then cast. Let me fix that.
0:21
Read
/work/solve.py
1 import geopandas as gpd
2 import pandas as pd
3 from shapely import wkt
4
5 # Read the CSV
6 df = pd.read_csv('/work/nyc_addresses.csv')
7
8 # Parse geometry from WKT
9 df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
10
11 # Create GeoDataFrame with EPSG:4326
12 gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')
13
14 # Drop the raw WKT column (no longer needed)
15 gdf = gdf.drop(columns=['geometry_wkt'])
16
17 # Cast recorded_at to timestamp[us] (microsecond precision)
18 gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
19
20 # Cast unit_count to int32
21 gdf['unit_count'] = gdf['unit_count'].astype('int32')
22
23 # Ensure all other columns are string
24 string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
25 for col in string_cols:
26 # Fill NaN with empty string before converting to avoid "nan" strings
27 gdf[col] = gdf[col].fillna('').astype('string')
28
29 # Write to GeoParquet
30 gdf.to_parquet('/work/addresses.geoparquet', index=False)
31
32 print("Conversion complete. File written to /work/addresses.geoparquet")
33
34 # Quick validation
35 check = gpd.read_parquet('/work/addresses.geoparquet')
36 print(f"Rows: {len(check)}")
37 print(f"CRS: {check.crs}")
38 print(f"Geometry type: {check.geometry.type.iloc[0]}")
39 print(f"recorded_at dtype: {check['recorded_at'].dtype}")
40 print(f"unit_count dtype: {check['unit_count'].dtype}")
41 print(f"All other columns are string: {all(check[c].dtype.name == 'string' for c in string_cols)}")
42 print(check.dtypes)
0:26
The timestamps are timezone-aware (they have "Z" suffix = UTC). I need to convert to timezone-naive datetime64[us] by calling tz_localize(None) after converting to UTC.
0:26
Edit
/work/solve.py
Edited /work/solve.py.
0:30
Wait, actually the parquet spec has `timestamp[us]` which is timezone-naive. But the user might want timezone-naive or timezone-aware. The `WHERE recorded_at > '2024-01-01'` comparison — if recorded_at is stored as `timestamp[us]` (which in parquet is typically timezone-naive), then a string literal comparison would work. Let me think... The task says: `WHERE recorded_at > '2024-01-01'` and `SUM(unit_count)` work without CASTs. So they want `recorded_at` as `timestamp[us]` (which is the parquet logical type for timestamp without timezone). So stripping timezone is correct. Let me run the script now.
0:30
Bash
cd /work && python solve.py
Conversion complete. File written to /work/addresses.geoparquet
Rows: 1056
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "GeographicCRS", "name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}
Geometry type: Point
recorded_at dtype: datetime64[us]
unit_count dtype: int32
All other columns are string: True
id string
country string
postcode string
street string
number string
unit string
postal_city string
recorded_at datetime64[us]
unit_count int32
geometry geometry
dtype: object
0:37
Let me verify the output file exists and do a final sanity check.
0:37
Bash
ls -lh /work/addresses.geoparquet
-rw-r--r-- 1 agent agent 84K Jun 18 14:16 /work/addresses.geoparquet
0:37
Bash
cd /work && python -c "
cd /work && python -c "
import duckdb
con = duckdb.connect()
con.execute('INSTALL spatial; LOAD spatial;')
con.execute(\"SELECT recorded_at, unit_count FROM 'addresses.geoparquet' WHERE recorded_at > '2024-01-01' LIMIT 5\")
print(con.fetchall())
con.execute(\"SELECT SUM(unit_count) FROM 'addresses.geoparquet'\")
print('SUM(unit_count):', con.fetchone()[0])
"
Traceback (most recent call last):
File "<string>", line 5, in <module>
con.execute("SELECT recorded_at, unit_count FROM 'addresses.geoparquet' WHERE recorded_at > '2024-01-01' LIMIT 5")
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_duckdb.BinderException: Binder Error: No extension found that is capable of reading the file "addresses.geoparquet"
* If this file is a supported file format you can explicitly use the reader functions, such as read_csv, read_json or read_parquet
0:43
The duckdb query needs to use `read_parquet` function or `ST_Read`. Let me fix that.
0:43
Bash
cd /work && python -c "
cd /work && python -c "
import duckdb
con = duckdb.connect()
con.execute('INSTALL spatial; LOAD spatial;')
con.execute(\"SELECT recorded_at, unit_count FROM read_parquet('addresses.geoparquet') WHERE recorded_at > '2024-01-01' LIMIT 5\")
print(con.fetchall())
con.execute(\"SELECT SUM(unit_count) FROM read_parquet('addresses.geoparquet')\")
print('SUM(unit_count):', con.fetchone()[0])
"
[(datetime.datetime(2024, 1, 12, 7, 13, 17), 0), (datetime.datetime(2024, 1, 23, 14, 26, 34), 0), (datetime.datetime(2024, 2, 3, 21, 39, 51), 0), (datetime.datetime(2024, 2, 14, 4, 52, 8), 1), (datetime.datetime(2024, 2, 25, 11, 5, 25), 0)] SUM(unit_count): 1566
0:49
The conversion works perfectly. Let me verify the results: 1. **Geometry**: Point in EPSG:4326 ✅ (coordinates are in degrees, ~-74 to -73, ~40 to 41) 2. **recorded_at**: `datetime64[us]` (timestamp[us]) ✅ — DuckDB reads it as datetime.datetime objects 3. **unit_count**: `int32` ✅ 4. **All other columns**: string ✅ 5. **WHERE recorded_at > '2024-01-01'** works without CASTs ✅ 6. **SUM(unit_count)** works without CASTs ✅ The file is ready.
0:49