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 /work
nyc_addresses.csv
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Bash
head -n 5 /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)"
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Write
/work/convert.py
content · 46 lines · py
import pandas as pd
import geopandas as gpd
from shapely import wkt
# Load the CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Convert recorded_at to datetime (timestamp[us])
df['recorded_at'] = pd.to_datetime(df['recorded_at'])
# Convert unit_count to int32
df['unit_count'] = df['unit_count'].astype('int32')
# Create geometry column from geometry_wkt
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# Drop the original geometry_wkt column
df = df.drop(columns=['geometry_wkt'])
# Convert to GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# Cast all other columns to string as requested
# We want to keep 'recorded_at', 'unit_count', and 'geometry' with their specific types.
# The request says "all other columns as string".
# Columns: id, country, postcode, street, number, unit, postal_city, recorded_at, unit_count, geometry
# Let's check types.
# Explicitly identify non-special columns
special_cols = ['recorded_at', 'unit_count', 'geometry']
other_cols = [col for col in gdf.columns if col not in special_cols]
for col in other_cols:
gdf[col] = gdf[col].astype(str)
# Reorder columns if necessary, but usually it's fine.
# Let's ensure 'geometry' is the last or expected column in GeoParquet if required,
# but GeoPandas handles it.
# Save to GeoParquet
gdf.to_parquet('/work/addresses.geoparquet')
print("Conversion successful.")
print("Columns and types:")
print(gdf.dtypes)
Wrote /work/convert.py (1441 chars).
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Bash
python /work/convert.py
Conversion successful. Columns and types: 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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Bash
python -c "import duckdb; con = duckdb.connect(); print(con.execute(\"SELECT * F...
python -c "import duckdb; con = duckdb.connect(); print(con.execute(\"SELECT * FROM read_parquet('/work/addresses.geoparquet') WHERE recorded_at > '2024-01-01' LIMIT 5\").df())"
python -c "import duckdb; con = duckdb.connect(); print(con.execute(\"SELECT SUM(unit_count) FROM read_parquet('/work/addresses.geoparquet')\").df())"
id ... geometry 0 00587e13-6253-485d-9258-e47d43af7559 ... [1, 1, 0, 0, 0, 187, 37, 222, 166, 228, 127, 8... 1 0070f126-d36a-43b3-b767-a8fbb7156602 ... [1, 1, 0, 0, 0, 95, 155, 141, 149, 24, 128, 82... 2 00a8fc38-5d9a-42da-94a9-7d2f4101f82c ... [1, 1, 0, 0, 0, 6, 37, 113, 251, 138, 127, 82,... 3 00f815af-51f6-44c2-b0d6-ee23092d14b2 ... [1, 1, 0, 0, 0, 31, 193, 232, 77, 32, 129, 82,... 4 014806e3-0deb-4be3-abd5-2be10dedf40c ... [1, 1, 0, 0, 0, 95, 136, 139, 146, 70, 128, 82... [5 rows x 10 columns] sum(unit_count) 0 1566.0
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Bash
python -c "import duckdb; con = duckdb.connect(); print(con.execute(\"SELECT typ...
typeof(recorded_at) typeof(unit_count) 0 TIMESTAMP WITH TIME ZONE INTEGER
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Bash
python -c "import duckdb; con = duckdb.connect(); print(con.execute(\"SELECT * F...
id ... geometry 0 0005eda0-2540-4e36-bc81-dfb52e323d99 ... [1, 1, 0, 0, 0, 23, 63, 50, 194, 128, 127, 82,... [1 rows x 10 columns]
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Write
/work/convert.py
content · 49 lines · py
import pandas as pd
import geopandas as gpd
from shapely import wkt
# Load the CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Convert recorded_at to datetime
# The user wants timestamp[us]. pandas to_datetime produces datetime64[ns].
# Parquet/DuckDB will treat datetime64[ns] as timestamp.
# However, to be precise with 'us' (microseconds) if possible:
df['recorded_at'] = pd.to_datetime(df['recorded_at']).dt.floor('us')
# Convert unit_count to int32
df['unit_count'] = df['unit_count'].astype('int32')
# Create geometry column from geometry_wkt
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# Drop the original geometry_wkt column
df = df.drop(columns=['geometry_wkt'])
# Convert to GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# Explicitly handle column types
# We need 'recorded_at' as timestamp[us], 'unit_count' as int32
# All other columns as string.
# Identify columns to be strings
special_cols = ['recorded_at', 'unit_count', 'geometry']
other_cols = [col for col in gdf.columns if col not in special_cols]
for col in other_cols:
gdf[col] = gdf[col].astype(str)
# Re-verify 'recorded_at' is datetime
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at'])
# Re-verify 'unit_count' is int32
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# Save to GeoParquet
gdf.to_parquet('/work/addresses.geoparquet')
print("Conversion successful.")
print("Columns and types:")
print(gdf.dtypes)
Wrote /work/convert.py (1459 chars).
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Bash
python /work/convert.py
Conversion successful. Columns and types: 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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