fio-l1-nyc-csvwkt-addresses
Map (reference ← swipe → agent)
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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.
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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
nyc_addresses.csv
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Bash
head -n 5 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)"
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Write
solve.py
content · 43 lines · py
import pandas as
import pandas as pd
import geopandas as gpd
from shapely import wkt
# Read the CSV
df = pd.read_csv('nyc_addresses.csv')
# 1. Convert geometry_wkt to geometry
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# 2. Create GeoDataFrame with EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 3. Type conversions
# recorded_at as timestamp[us]
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at'], format='ISO8601')
# unit_count as int32
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# All other columns (except geometry and the specifically typed ones) as string
# We'll drop 'geometry_wkt' as it's redundant now
cols_to_string = [c for c in gdf.columns if c not in ['geometry', 'recorded_at', 'unit_count']]
for col in cols_to_string:
gdf[col] = gdf[col].astype(str)
# Drop the old WKT column to keep it clean
gdf = gdf.drop(columns=['geometry_wkt'])
# Reorder columns to ensure geometry is handled correctly by GeoParquet writer if needed,
# but geopandas usually handles it.
# The requirement is to save as addresses.geoparquet
# Note: GeoParquet requires the geometry column to be present.
# We also need to ensure the timestamp is actually datetime objects so the parquet writer
# uses the correct logical type.
gdf.to_parquet('addresses.geoparquet', index=False)
print("Successfully converted nyc_addresses.csv to addresses.geoparquet")
print(gdf.dtypes)
print(gdf.head())
Wrote solve.py (1449 chars).
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Bash
python solve.py
File "/work/solve.py", line 1
import pandas as
^
SyntaxError: invalid syntax
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Edit
solve.py
Edited solve.py.
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Bash
python solve.py
Successfully converted nyc_addresses.csv to addresses.geoparquet
id str
country str
postcode str
street str
number str
unit str
postal_city str
recorded_at datetime64[us, UTC]
unit_count int32
geometry geometry
dtype: object
id ... geometry
0 0005eda0-2540-4e36-bc81-dfb52e323d99 ... POINT (-73.99223 40.71616)
1 00587e13-6253-485d-9258-e47d43af7559 ... POINT (-73.99833 40.71204)
2 0070f126-d36a-43b3-b767-a8fbb7156602 ... POINT (-74.0015 40.70733)
3 00a8fc38-5d9a-42da-94a9-7d2f4101f82c ... POINT (-73.99286 40.70268)
4 00f815af-51f6-44c2-b0d6-ee23092d14b2 ... POINT (-74.0176 40.71024)
[5 rows x 10 columns]
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Write
solve.py
content · 41 lines · py
import pandas as pd
import geopandas as gpd
from shapely import wkt
import pyarrow as pa
import pyarrow.parquet as pq
# Read the CSV
df = pd.read_csv('nyc_addresses.csv')
# 1. Convert geometry_wkt to geometry
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# 2. Create GeoDataFrame with EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 3. Type conversions
# recorded_at as timestamp[us]
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at'], format='ISO8601')
# unit_count as int32
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# All other columns (except geometry and the specifically typed ones) as string
# We'll drop 'geometry_wkt' as it's redundant now
cols_to_string = [c for c in gdf.columns if c not in ['geometry', 'recorded_at', 'unit_count']]
for col in cols_to_string:
gdf[col] = gdf[col].astype(str)
# Drop the old WKT column
gdf = gdf.drop(columns=['geometry_wkt'])
# Final check of types before saving
print("Types before saving:")
print(gdf.dtypes)
# Save as GeoParquet
# We use the geopandas to_parquet method which handles the GeoParquet metadata
gdf.to_parquet('addresses.geoparquet', index=False)
print("\nSuccessfully converted nyc_addresses.csv to addresses.geoparquet")
Wrote solve.py (1249 chars).
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Bash
python solve.py
Types before saving: id str country str postcode str street str number str unit str postal_city str recorded_at datetime64[us, UTC] unit_count int32 geometry geometry dtype: object Successfully converted nyc_addresses.csv to addresses.geoparquet
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Bash
duckdb -c "SELECT * FROM 'addresses.geoparquet' LIMIT 5;"
bash: line 1: duckdb: command not found
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Bash
python -c "import duckdb; print('duckdb is available')"
duckdb is available
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Write
verify.py
content · 17 lines · py
import duckdb
# Check the schema and types using DuckDB
con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")
print("Table Schema:")
con.execute("DESCRIBE SELECT * FROM 'addresses.geoparquet'").show()
print("\nTesting Query: WHERE recorded_at > '2024-01-01'")
res = con.execute("SELECT count(*) FROM 'addresses.geoparquet' WHERE recorded_at > '2024-01-01'").fetchone()
print(f"Count: {res[0]}")
print("\nTesting Query: SUM(unit_count)")
res = con.execute("SELECT SUM(unit_count) FROM 'addresses.geoparquet'").fetchone()
print(f"Sum: {res[0]}")
Wrote verify.py (563 chars).
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Bash
python verify.py
Table Schema:
Traceback (most recent call last):
File "/work/verify.py", line 8, in <module>
con.execute("DESCRIBE SELECT * FROM 'addresses.geoparquet'").show()
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_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
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Write
verify.py
content · 17 lines · py
import duckdb
# Check the schema and types using DuckDB
con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")
print("Table Schema (using read_parquet):")
con.execute("DESCRIBE SELECT * FROM read_parquet('addresses.geoparquet')").show()
print("\nTesting Query: WHERE recorded_at > '2024-01-01'")
res = con.execute("SELECT count(*) FROM read_parquet('addresses.geoparquet') WHERE recorded_at > '2024-01-01'").fetchone()
print(f"Count: {res[0]}")
print("\nTesting Query: SUM(unit_count)")
res = con.execute("SELECT SUM(unit_count) FROM read_parquet('addresses.geoparquet')").fetchone()
print(f"Sum: {res[0]}")
Wrote verify.py (626 chars).
0:38
Bash
python verify.py
Table Schema (using read_parquet):
Traceback (most recent call last):
File "/work/verify.py", line 8, in <module>
con.execute("DESCRIBE SELECT * FROM read_parquet('addresses.geoparquet')").show()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: '_duckdb.DuckDBPyConnection' object has no attribute 'show'
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Write
verify.py
content · 18 lines · py
import duckdb
# Check the schema and types using DuckDB
con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")
print("Table Schema (using read_parquet):")
df_schema = con.execute("DESCRIBE SELECT * FROM read_parquet('addresses.geoparquet')").df()
print(df_schema)
print("\nTesting Query: WHERE recorded_at > '2024-01-01'")
res = con.execute("SELECT count(*) FROM read_parquet('addresses.geoparquet') WHERE recorded_at > '2024-01-01'").fetchone()
print(f"Count: {res[0]}")
print("\nTesting Query: SUM(unit_count)")
res = con.execute("SELECT SUM(unit_count) FROM read_parquet('addresses.geoparquet')").fetchone()
print(f"Sum: {res[0]}")
Wrote verify.py (653 chars).
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Bash
python verify.py
Table Schema (using read_parquet):
column_name column_type null key default extra
0 id VARCHAR YES None None None
1 country VARCHAR YES None None None
2 postcode VARCHAR YES None None None
3 street VARCHAR YES None None None
4 number VARCHAR YES None None None
5 unit VARCHAR YES None None None
6 postal_city VARCHAR YES None None None
7 recorded_at TIMESTAMP WITH TIME ZONE YES None None None
8 unit_count INTEGER YES None None None
9 geometry GEOMETRY('EPSG:4326') YES None None None
Testing Query: WHERE recorded_at > '2024-01-01'
Count: 1055
Testing Query: SUM(unit_count)
Sum: 1566
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