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 · 53 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pandas", "pyarrow", "pyogrio"]
# ///
import pandas as pd
import geopandas as gpd
from shapely import wkt
def main():
input_file = 'nyc_addresses.csv'
output_file = 'addresses.geoparquet'
print(f"Reading {input_file}...")
df = pd.read_csv(input_file)
# 1. Parse geometry
print("Parsing geometry...")
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# Create GeoDataFrame with EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 2. Handle columns
# recorded_at as timestamp[us]
print("Converting recorded_at to timestamp...")
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
# unit_count as int32
print("Converting unit_count to int32...")
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# All other columns as string (excluding geometry)
print("Converting other columns to string...")
# Columns to keep as specific types: geometry, recorded_at, unit_count
# Everything else in the original df (and the geometry column itself is handled by GeoPandas)
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).replace(['nan', 'None', '<NA>'], '')
# Remove the original WKT column as it's redundant now
if 'geometry_wkt' in gdf.columns:
gdf = gdf.drop(columns=['geometry_wkt'])
# 3. Save to GeoParquet
print(f"Saving to {output_file}...")
gdf.to_parquet(output_file)
print("Done!")
if __name__ == "__main__":
main()
file_path must be inside the working directory (/work): '/home/nhp/project/benchmark/harness/.sessions/c982a78144f24820987134fdf2a70606/solve.py'
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Write
solve.py
content · 63 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pandas", "pyarrow", "pyogrio"]
# ///
import pandas as pd
import geopandas as gpd
from shapely import wkt
def main():
input_file = 'nyc_addresses.csv'
output_file = 'addresses.geoparquet'
print(f"Reading {input_file}...")
df = pd.read_csv(input_file)
# 1. Parse geometry
print("Parsing geometry...")
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
# Create GeoDataFrame with EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:4326")
# 2. Handle columns
# recorded_at as timestamp[us]
print("Converting recorded_at to timestamp...")
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
# unit_count as int32
print("Converting unit_count to int32...")
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# All other columns as string (excluding geometry, recorded_at, unit_count)
print("Converting other columns to string...")
# Get current columns
all_cols = gdf.columns.tolist()
# Exclude the ones we explicitly want as other types
# Note: geometry is a special type in GeoDataFrame, but it's in the columns list.
cols_to_string = [c for c in all_cols if c not in ['geometry', 'recorded_at', 'unit_count']]
for col in cols_to_string:
# Convert to string and handle potential NaNs to avoid "nan" string if possible,
# but the requirement says "all other columns as string".
# Usually, in SQL/Parquet, a string column can have nulls.
# Let's ensure they are strings.
gdf[col] = gdf[col].astype(str)
# Cleaning up typical pandas string conversion artifacts for nulls
gdf.loc[gdf[col].isin(['nan', 'None', '<NA>', 'NaN']), col] = None
# Remove the original WKT column as it's redundant now
if 'geometry_wkt' in gdf.columns:
gdf = gdf.drop(columns=['geometry_wkt'])
# 3. Save to GeoParquet
print(f"Saving to {output_file}...")
# Using engine='pyogrio' is often faster and more robust for GeoParquet
gdf.to_parquet(output_file, index=False)
print("Done!")
if __name__ == "__main__":
main()
Wrote solve.py (2238 chars).
0:21
Bash
uv run solve.py
Reading nyc_addresses.csv... Parsing geometry... Converting recorded_at to timestamp...
Installed 11 packages in 290ms
Traceback (most recent call last):
File "/work/solve.py", line 62, in <module>
main()
~~~~^^
File "/work/solve.py", line 27, in main
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at']).astype('datetime64[us]')
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/generic.py", line 6541, in astype
new_data = self._mgr.astype(dtype=dtype, errors=errors)
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/internals/managers.py", line 614, in astype
return self.apply("astype", dtype=dtype, errors=errors)
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/internals/managers.py", line 445, in apply
applied = getattr(b, f)(**kwargs)
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/internals/blocks.py", line 607, in astype
new_values = astype_array_safe(values, dtype, errors=errors)
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/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 "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/lib/python3.14/site-packages/pandas/core/dtypes/astype.py", line 182, in astype_array
values = values.astype(dtype, copy=copy)
File "/home/agent/.cache/uv/environments-v2/solve-a8173a6dea90dc14/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.
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Edit
solve.py
Edited solve.py.
0:30
Bash
uv run solve.py
Reading nyc_addresses.csv... Parsing geometry... Converting recorded_at to timestamp... Converting unit_count to int32... Converting other columns to string... Saving to addresses.geoparquet... Done!
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Bash
ls -F
addresses.geoparquet nyc_addresses.csv solve.py
0:37
Bash
python3 -c "import duckdb; print(duckdb.query(\"SELECT recorded_at, unit_count, ...
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5").to_df())
^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'duckdb'
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Bash
uv run python3 -c "import duckdb; print(duckdb.query(\"SELECT recorded_at, unit_...
Traceback (most recent call last):
File "<string>", line 1, in <module>
import duckdb; print(duckdb.query("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5").to_df())
^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'duckdb'
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Write
check_types.py
content · 11 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["duckdb", "duckdb-spatial"]
# ///
import duckdb
con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")
print(con.execute(\"SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5\").to_df())
Wrote check_types.py (319 chars).
0:42
Bash
uv run check_types.py
× No solution found when resolving script dependencies:
╰─▶ Because duckdb-spatial was not found in the package registry and you
require duckdb-spatial, we can conclude that your requirements are
unsatisfiable.
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Write
check_types.py
content · 11 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["duckdb"]
# ///
import duckdb
con = duckdb.connect()
con.execute("INSTALL spatial; LOAD spatial;")
print(con.execute(\"SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5\").to_df())
Wrote check_types.py (301 chars).
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Bash
uv run check_types.py
Installed 1 package in 16ms
File "/work/check_types.py", line 10
print(con.execute(\"SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5\").to_df())
^
SyntaxError: unexpected character after line continuation character
0:48
Edit
check_types.py
Edited check_types.py.
0:48
Bash
uv run check_types.py
Traceback (most recent call last):
File "/work/check_types.py", line 10, in <module>
print(con.execute("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM 'addresses.geoparquet' LIMIT 5").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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Write
check_types.py
content · 12 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["duckdb"]
# ///
import duckdb
con = duckdb.connect()
# duckdb can read parquet directly without spatial extension if we don't need geometry ops
# but we want to check the types of the columns.
print(con.execute("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM read_parquet('addresses.geoparquet') LIMIT 5").to_df())
Wrote check_types.py (407 chars).
0:57
Bash
uv run check_types.py
Traceback (most recent call last):
File "/work/check_types.py", line 11, in <module>
print(con.execute("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM read_parquet('addresses.geoparquet') LIMIT 5").to_df())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: '_duckdb.DuckDBPyConnection' object has no attribute 'to_df'
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Write
check_types.py
content · 11 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["duckdb", "pandas"]
# ///
import duckdb
con = duckdb.connect()
df = con.execute("SELECT recorded_at, unit_count, typeof(recorded_at), typeof(unit_count) FROM read_parquet('addresses.geoparquet') LIMIT 5").df()
print(df)
Wrote check_types.py (282 chars).
1:00
Bash
uv run check_types.py
recorded_at unit_count typeof(recorded_at) typeof(unit_count) 0 2024-01-01 00:00:00 0 TIMESTAMP INTEGER 1 2024-01-12 07:13:17 0 TIMESTAMP INTEGER 2 2024-01-23 14:26:34 0 TIMESTAMP INTEGER 3 2024-02-03 21:39:51 0 TIMESTAMP INTEGER 4 2024-02-14 04:52:08 1 TIMESTAMP INTEGER
Installed 4 packages in 263ms
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Bash
# Final check: also check column names and other columns' types
# Final check: also check column names and other columns' types
uv run python3 -c "import pandas as pd; df = pd.read_parquet('addresses.geoparquet'); print(df.dtypes); print(df.head())"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import pandas as pd; df = pd.read_parquet('addresses.geoparquet'); print(df.dtypes); print(df.head())
^^^^^^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'pandas'
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Write
final_check.py
content · 12 lines · py
# /// script
# requires-python = ">=3.12"
# dependencies = ["pandas", "pyarrow"]
# ///
import pandas as pd
df = pd.read_parquet('addresses.geoparquet')
print("--- Dtypes ---")
print(df.dtypes)
print("\n--- Head ---")
print(df.head())
Wrote final_check.py (235 chars).
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Bash
uv run final_check.py
--- Dtypes ---
id str
country str
postcode str
street str
number str
unit str
postal_city str
recorded_at datetime64[us]
unit_count int32
geometry object
dtype: object
--- Head ---
id ... geometry
0 0005eda0-2540-4e36-bc81-dfb52e323d99 ... b'\x01\x01\x00\x00\x00\x17?2\xc2\x80\x7fR\xc0y...
1 00587e13-6253-485d-9258-e47d43af7559 ... b'\x01\x01\x00\x00\x00\xbb%\xde\xa6\xe4\x7fR\x...
2 0070f126-d36a-43b3-b767-a8fbb7156602 ... b'\x01\x01\x00\x00\x00_\x9b\x8d\x95\x18\x80R\x...
3 00a8fc38-5d9a-42da-94a9-7d2f4101f82c ... b'\x01\x01\x00\x00\x00\x06%q\xfb\x8a\x7fR\xc0\...
4 00f815af-51f6-44c2-b0d6-ee23092d14b2 ... b'\x01\x01\x00\x00\x00\x1f\xc1\xe8M \x81R\xc0\...
[5 rows x 10 columns]
Installed 5 packages in 212ms
1:13
Bash
ls -F
addresses.geoparquet check_types.py final_check.py nyc_addresses.csv solve.py
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