fio-l1-nyc-csvwkt-addresses

Status: done
Score: 1.000
Duration: 0:30
Cost: 0.31¢
Model: google/gemma-4-26b-a4b-it

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 · 50 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["pandas", "geopandas", "shapely", "pyarrow", "pyproj"]
# ///

import pandas as pd
import geopandas as gpd
from shapely import wkt

def convert_nyc_addresses(input_csv, output_parquet):
    # Load the CSV
    df = pd.read_csv(input_csv)

    # 1. Convert geometry_wkt to actual geometry
    df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
    
    # Create GeoDataFrame with EPSG:4326
    gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")

    # 2. Convert recorded_at to timestamp[us]
    # Pandas datetime objects are generally handled well by pyarrow/geoparquet
    gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).dt.floor('us')

    # 3. Convert unit_count to int32
    gdf['unit_count'] = gdf['unit_count'].astype('int32')

    # 4. All other columns as string (except geometry and the ones we just handled)
    # The columns we want to keep as string are:
    # id, country, postcode, street, number, unit, postal_city
    # Note: geometry is handled by GeoPandas
    
    cols_to_string = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
    for col in cols_to_string:
        if col in gdf.columns:
            gdf[col] = gdf[col].astype(str).replace(['nan', 'None'], '')

    # Remove the original WKT column
    if 'geometry_wkt' in gdf.columns:
        gdf = gdf.drop(columns=['geometry_wkt'])

    # 5. Save to GeoParquet
    # GeoParquet requires the geometry column to be present and correctly formatted
    gdf.to_parquet(output_parquet, index=False)
    print(f"Successfully converted {input_csv} to {output_parquet}")
    print("Schema info:")
    print(gdf.dtypes)

if __name__ == "__main__":
    convert_nyc_addresses('nyc_addresses.csv', 'addresses.geoparquet')

Wrote /home/nhp/project/benchmark/harness/.sessions/308432053915473886d0df954fe6864e/solve.py (1793 chars).
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Bash uv run /home/nhp/project/benchmark/harnesssolve.py 
Successfully converted nyc_addresses.csv to addresses.geoparquet
Schema info:
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
Installed 11 packages in 176ms
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Write solve.py
content · 52 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["pandas", "geopandas", "shapely", "pyarrow", "pyproj"]
# ///

import pandas as pd
import geopandas as gpd
from shapely import wkt

def convert_nyc_addresses(input_csv, output_parquet):
    # Load the CSV
    df = pd.read_csv(input_csv)

    # Convert geometry_wkt to actual geometry
    df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
    
    # Create GeoDataFrame with EPSG:4326
    gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")

    # 2. Convert recorded_at to timestamp[us]
    gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).dt.floor('us')

    # 3. Convert unit_count to int32
    gdf['unit_count'] = gdf['unit_count'].astype('int32')

    # 4. All other columns as string (except geometry and the ones we just handled)
    # The columns we want to keep as string are:
    # id, country, postcode, street, number, unit, postal_city
    
    # Identify all columns that are not 'geometry', 'recorded_at', or 'unit_count'
    # and not the original 'geometry_wkt'
    exclude = {'geometry', 'recorded_at', 'unit_count', 'geometry_wkt'}
    cols_to_string = [col for col in gdf.columns if col not in exclude]
    
    for col in cols_to_string:
        # We must convert to string but be careful with NaNs
        gdf[col] = gdf[col].astype(str).replace(['nan', 'None'], '')

    # Remove the original WKT column
    if 'geometry_wkt' in gdf.columns:
        gdf = gdf.drop(columns=['geometry_wkt'])

    # 5. Save to GeoParquet
    # We use pyarrow engine for more predictable schema
    gdf.to_parquet(output_parquet, index=False)
    print(f"Successfully converted {input_csv} to {output_parquet}")
    print("Schema info:")
    print(gdf.dtypes)

if __name__ == "__main__":
    convert_nyc_addresses('nyc_addresses.csv', 'addresses.geoparquet')

Wrote /home/nhp/project/benchmark/harness/.sessions/308432053915473886d0df954fe6864e/solve.py (1844 chars).
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Bash uv run /home/nhp/project/benchmark/harnesssolve.py 
Successfully converted nyc_addresses.csv to addresses.geoparquet
Schema info:
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
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Write solve.py
content · 47 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["pandas", "geopandas", "shapely", "pyarrow", "pyproj"]
# ///

import pandas as pd
import geopandas as gpd
from shapely import wkt

def convert_nyc_addresses(input_csv, output_parquet):
    # Load the CSV
    df = pd.read_csv(input_csv)

    # 1. Convert geometry_wkt to actual geometry
    df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
    
    # Create GeoDataFrame with EPSG:4326
    gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")

    # 2. Convert recorded_at to timestamp[us]
    # Use UTC to ensure it's a proper timestamp
    gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at'], utc=True).dt.floor('us')

    # 3. Convert unit_count to int32
    gdf['unit_count'] = gdf['unit_count'].astype('int32')

    # 4. All other columns as string (except geometry and the ones we just handled)
    exclude = {'geometry', 'recorded_at', 'unit_count', 'geometry_wkt'}
    cols_to_string = [col for col in gdf.columns if col not in exclude]
    
    for col in cols_to_string:
        # Fill NaNs with empty string before converting to string to avoid "nan" strings
        gdf[col] = gdf[col].fillna('').astype(str)

    # Remove the original WKT column
    if 'geometry_wkt' in gdf.columns:
        gdf = gdf.drop(columns=['geometry_wkt'])

    # 5. Save to GeoParquet
    gdf.to_parquet(output_parquet, index=False)
    print(f"Successfully converted {input_csv} to {output_parquet}")
    print("Schema info:")
    print(gdf.dtypes)

if __name__ == "__main__":
    convert_nyc_addresses('nyc_addresses.csv', 'addresses.geoparquet')

Wrote /home/nhp/project/benchmark/harness/.sessions/308432053915473886d0df954fe6864e/solve.py (1617 chars).
0:24
Bash uv run /home/nhp/project/benchmark/harnesssolve.py 
Successfully converted nyc_addresses.csv to addresses.geoparquet
Schema info:
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
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