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
ls -F
nyc_addresses.csv
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Bash
head -n 5 nyc_addresses.csv
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
/work/solve.py
content · 38 lines · py
import pandas as pd
import geopandas as gpd
from shapely import wkt
# 1. Load the CSV
df = pd.read_csv('nyc_addresses.csv')
# 2. Convert recorded_at to datetime (timestamp[us])
df['recorded_at'] = pd.to_datetime(df['recorded_at'], format='ISO8601')
# 3. Convert unit_count to int32
df['unit_count'] = df['unit_count'].astype('int32')
# 4. Create GeoDataFrame from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 5. Convert all other columns to string (except geometry, recorded_at, and unit_count)
# The user wants: recorded_at as timestamp[us], unit_count as int32, all other columns as string.
# Note: the geometry is a special column in GeoParquet.
cols_to_fix = [c for c in gdf.columns if c not in ['geometry', 'recorded_at', 'unit_count']]
for col in cols_to_fix:
gdf[col] = gdf[col].astype(str)
# 6. Drop the original WKT column if it's no longer needed,
# but strictly speaking, the user didn't say to drop it.
# However, in GeoParquet, the 'geometry' column is the standard.
# Let's check what columns are there.
print("Columns before dropping WKT:", gdf.columns.tolist())
if 'geometry_wkt' in gdf.columns:
gdf = gdf.drop(columns=['geometry_wkt'])
# 7. Save to GeoParquet
gdf.to_parquet('addresses.geoparquet')
print("Successfully saved to addresses.geoparquet")
print("Dtypes:\n", gdf.dtypes)
Wrote /work/solve.py (1396 chars).
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Bash
python solve.py
python solve.py
Columns before dropping WKT: ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city', 'recorded_at', 'unit_count', 'geometry_wkt', 'geometry'] Successfully saved to addresses.geoparquet Dtypes: 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
/work/solve.py
content · 34 lines · py
import pandas as pd
import geopandas as gpd
from shapely import wkt
# 1. Load the CSV
df = pd.read_csv('nyc_addresses.csv')
# 2. Convert recorded_at to datetime (timestamp[us])
# The user specified timestamp[us]. In pandas/pyarrow, datetime64[us] is what we want.
df['recorded_at'] = pd.to_datetime(df['recorded_at'], format='ISO8601')
# 3. Convert unit_count to int32
df['unit_count'] = df['unit_count'].astype('int32')
# 4. Create GeoDataFrame from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 5. Drop the original WKT column
if 'geometry_wkt' in gdf.columns:
gdf = gdf.drop(columns=['geometry_wkt'])
# 6. Convert all other columns to string
# Explicitly: recorded_at (datetime), unit_count (int32), geometry (geometry), everything else (string)
other_cols = [c for c in gdf.columns if c not in ['geometry', 'recorded_at', 'unit_count']]
for col in other_cols:
gdf[col] = gdf[col].astype(str)
# 7. Save to GeoParquet
gdf.to_parquet('addresses.geoparquet')
print("Successfully saved to addresses.geoparquet")
print("Final Schema/Dtypes:")
print(gdf.dtypes)
Wrote /work/solve.py (1152 chars).
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Bash
python solve.py
python solve.py
Successfully saved to addresses.geoparquet Final Schema/Dtypes: 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; print(duckdb.query('SELECT recorded_at, unit_count FRO...
python -c "import duckdb; print(duckdb.query('SELECT recorded_at, unit_count FROM \"addresses.geoparquet\" WHERE recorded_at > \"2024-01-01\" AND unit_count > 0').to_df())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query('SELECT recorded_at, unit_count FROM "addresses.geoparquet" WHERE recorded_at > "2024-01-01" AND unit_count > 0').to_df())
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_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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Bash
python -c "import duckdb; print(duckdb.query('SELECT recorded_at, unit_count FRO...
python -c "import duckdb; print(duckdb.query('SELECT recorded_at, unit_count FROM \"addresses.geoparquet\" WHERE recorded_at > \"2024-01-01\"').to_df())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query('SELECT recorded_at, unit_count FROM "addresses.geoparquet" WHERE recorded_at > "2024-01-01"').to_df())
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_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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Bash
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addre...
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addresses.geoparquet\") WHERE recorded_at > \"2024-01-01\"').to_df())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query('SELECT * FROM read_parquet("addresses.geoparquet") WHERE recorded_at > "2024-01-01"').to_df())
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_duckdb.BinderException: Binder Error: Referenced column "2024-01-01" not found in FROM clause!
Candidate bindings: "country"
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Bash
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addre...
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addresses.geoparquet\") WHERE recorded_at > \"2024-01-01\"').to_df())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query('SELECT * FROM read_parquet("addresses.geoparquet") WHERE recorded_at > "2024-01-01"').to_df())
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_duckdb.BinderException: Binder Error: Referenced column "2024-01-01" not found in FROM clause!
Candidate bindings: "country"
1:02
Bash
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addre...
python -c "import duckdb; print(duckdb.query('SELECT * FROM read_parquet(\"addresses.geoparquet\") WHERE recorded_at > CAST(\"2024-01-01\" AS TIMESTAMP)').to_df())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query('SELECT * FROM read_parquet("addresses.geoparquet") WHERE recorded_at > CAST("2024-01-01" AS TIMESTAMP)').to_df())
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_duckdb.BinderException: Binder Error: Referenced column "2024-01-01" not found in FROM clause!
Candidate bindings: "country"
1:14
Bash
python -c "import duckdb; print(duckdb.query(\"SELECT * FROM read_parquet('addre...
python -c "import duckdb; print(duckdb.query(\"SELECT * FROM read_parquet('addresses.geoparquet') WHERE recorded_at > '2024-01-01' LIMIT 5\").to_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]
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Bash
python -c "import duckdb; print(duckdb.query(\"SELECT SUM(unit_count) FROM read_...
python -c "import duckdb; print(duckdb.query(\"SELECT SUM(unit_count) FROM read_parquet('addresses.geoparquet')\").to_df())"
sum(unit_count) 0 1566.0
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