fio-l1-nyc-csvwkt-addresses
Map (reference ← swipe → agent)
0:00
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.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:09
Let me start by exploring the working directory and understanding the structure of the SQL export file.
0:09
Bash
ls -la /work/
total 144 drwxr-xr-x 1 agent agent 34 Jun 16 01:58 . drwxr-xr-x 1 root root 14 Jun 16 01:58 .. -rw-r--r-- 1 agent agent 143422 Jun 16 01:58 nyc_addresses.csv
0:26
Let's look at the CSV file to understand its structure and column types.
0:26
Read
/work/nyc_addresses.csv
1 "id","country","postcode","street","number","unit","postal_city","recorded_at","unit_count","geometry_wkt"
2 "0005eda0-2540-4e36-bc81-dfb52e323d99","US","10002","ALLEN ST","37","1/2","","2024-01-01T00:00:00Z","0","POINT (-73.9922338 40.716163)"
3 "00587e13-6253-485d-9258-e47d43af7559","US","10038","MADISON ST","45","","","2024-01-12T07:13:17Z","0","POINT (-73.9983308 40.71204)"
4 "0070f126-d36a-43b3-b767-a8fbb7156602","US","10038","PECK SLIP","44","","","2024-01-23T14:26:34Z","0","POINT (-74.0015005 40.7073293)"
5 "00a8fc38-5d9a-42da-94a9-7d2f4101f82c","US","11201","FRONT ST","11","","","2024-02-03T21:39:51Z","0","POINT (-73.9928578 40.702681)"
6 "00f815af-51f6-44c2-b0d6-ee23092d14b2","US","10280","ALBANY ST","340","","","2024-02-14T04:52:08Z","1","POINT (-74.0175967 40.7102422)"
7 "014806e3-0deb-4be3-abd5-2be10dedf40c","US","10013","CHURCH ST","299","","","2024-02-25T11:05:25Z","0","POINT (-74.0043074 40.7193005)"
8 "018c3b29-04d1-4890-a12b-8098f6f0c7de","US","10013","WALKER ST","37","","","2024-03-07T18:18:42Z","0","POINT (-74.0040214 40.7192277)"
9 "01cbed19-2940-4b4a-9d90-6f0be7f1b604","US","10012","MOTT ST","184","","","2024-03-18T01:31:59Z","0","POINT (-73.9957225 40.7207067)"
10 "01fadb51-074c-4050-a670-4a615337d972","US","10002","E BROADWAY","53","","","2024-03-29T08:44:16Z","2","POINT (-73.9955533 40.7135006)"
11 "02338589-3333-46a3-8adb-84b4ab20dd49","US","10007","WORLD TRADE CENTER","1","","","2024-04-09T15:57:33Z","0","POINT (-74.0130135 40.7132465)"
12 "028bc5b7-7987-49b6-b764-be1f6f81f309","US","10012","THOMPSON ST","68","","","2024-04-20T22:10:50Z","0","POINT (-74.0030133 40.7242823)"
13 "02ad9ccb-a580-45ef-8488-d3b35b58f3e4","US","10014","AVE OF THE AMERICAS","260","","","2024-05-01T05:23:07Z","0","POINT (-74.0023323 40.7289513)"
14 "02eb28d4-4e3d-4eb0-a72d-b3795bdb5a5b","US","10012","WOOSTER ST","139","","","2024-05-12T12:36:24Z","3","POINT (-73.9998396 40.7258502)"
15 "031f3e1d-7b14-4f57-8b3f-50d3fb2242e7","US","10013","DESBROSSES ST","2","","","2024-05-23T19:49:41Z","0","POINT (-74.0084458 40.7234422)"
16 "037716a1-f77e-4fda-bb9f-5861994e15ef","US","10013","MOTT ST","85","","","2024-06-03T02:02:58Z","0","POINT (-73.9979789 40.7166695)"
17 "03b4681c-0c78-4483-9d20-714c8315ea11","US","10013","BAXTER ST","120","","","2024-06-14T09:15:15Z","0","POINT (-73.9991465 40.7180102)"
18 "0400947d-6748-449c-9ab9-177fa96ccaaf","US","10013","GRAND ST","154","","","2024-06-25T16:28:32Z","4","POINT (-73.9988115 40.7200596)"
19 "04382316-a3fe-4442-bef1-b0e1b64f0502","US","11201","MIDDAGH ST","12","","","2024-07-06T23:41:49Z","0","POINT (-73.9946485 40.7007669)"
20 "04858323-61a9-4293-a2bf-14d3e8f97359","US","10012","MAC DOUGAL ST","48","","","2024-07-17T06:54:06Z","0","POINT (-74.0026039 40.7273331)"
21 "04d5ee66-5723-4893-b10f-281e240f44d7","US","10002","HESTER ST","124","A","","2024-07-28T13:07:23Z","0","POINT (-73.9947517 40.7169841)"
22 "0523460c-2fa8-4e46-a2f9-32a6338c9996","US","10003","E 2 ST","8","","","2024-08-08T20:20:40Z","5","POINT (-73.9914287 40.7255015)"
23 "055bb440-332b-41e9-965f-65011adcb899","US","10013","BROADWAY","440","","","2024-08-19T03:33:57Z","0","POINT (-74.0011001 40.7201053)"
24 "05a1d228-5dbf-4339-ae06-3eeeb55b637c","US","10012","MULBERRY ST","247","","","2024-08-30T10:46:14Z","0","POINT (-73.9959353 40.7230647)"
25 "05eefdfd-eece-4b75-b983-605c7550a79e","US","10012","BROADWAY","648","","","2024-09-10T17:59:31Z","0","POINT (-73.9955221 40.7266978)"
26 "062c5f30-b919-491c-847c-c046603f6f4f","US","10013","MOTT ST","67","","","2024-09-21T00:12:48Z","6","POINT (-73.9982654 40.7161944)"
27 "068ed55c-f9dc-4b82-b931-7d26b54929d2","US","10013","N MOORE ST","43","","","2024-10-02T07:25:05Z","0","POINT (-74.0082641 40.7200255)"
28 "06b7f9d4-be62-4f8b-bb2a-614a7c6dbe8f","US","10013","RENWICK ST","15","","","2024-10-13T14:38:22Z","0","POINT (-74.0086515 40.7248328)"
29 "06ea3554-26cc-4954-a81f-736e51b626db","US","10013","BAXTER ST","89","","","2024-10-24T21:51:39Z","0","POINT (-73.9996297 40.7168333)"
30 "07193249-32ce-4761-9b50-d0a9bdf2b9dc","US","10013","CORTLANDT ALY","41","","","2024-11-04T04:04:56Z","7","POINT (-74.0014248 40.7187065)"
<system-reminder>Showed lines 1-30 of 1057. Re-call Read with offset=31 to continue, or raise limit.</system-reminder>
0:40
I can see the CSV file. Let me understand the requirements: 1. Convert the CSV to a GeoParquet file (`addresses.geoparquet`) 2. Point geometry in EPSG:4326 (from WKT column) 3. `recorded_at` as `timestamp[us]` 4. `unit_count` as `int32` 5. All other columns as string Let me write a Python script to do this.
0:40
Bash
wc -l /work/nyc_addresses.csv
1057 /work/nyc_addresses.csv
1:03
I have the CSV file with 1056 data rows + header. Let me analyze: Columns: id, country, postcode, street, number, unit, postal_city, recorded_at, unit_count, geometry_wkt Requirements: 1. Point geometry in EPSG:4326 (from geometry_wkt column which already has POINT in lat/lon) 2. `recorded_at` as `timestamp[us]` 3. `unit_count` as `int32` 4. All other columns as string 5. Output to `/work/addresses.geoparquet` I'll write a Python script using geopandas, pyarrow/pandas to create the GeoParquet file with proper schema.
1:03
Write
/work/solve.py
content · 90 lines · py
import geopandas as gpd
import pandas as pd
from shapely import wkt
import pyarrow as pa
import pyarrow.parquet as pq
# Read CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
df = df.drop(columns=['geometry_wkt'])
# Create GeoDataFrame with CRS EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')
# Build schema
# All columns except geometry, recorded_at, unit_count: string
# recorded_at: timestamp[us]
# unit_count: int32
fields = []
# Define the order of columns as they appear (keeping original order, but geometry last is typical for GeoParquet)
for col in gdf.columns:
if col == 'geometry':
continue
if col == 'recorded_at':
fields.append(pa.field(col, pa.timestamp('us')))
elif col == 'unit_count':
fields.append(pa.field(col, pa.int32()))
else:
fields.append(pa.field(col, pa.string()))
# Add geometry field as binary
fields.append(pa.field('geometry', pa.binary()))
schema = pa.schema(fields)
# Convert dataframe to pyarrow table with correct types
table = pa.Table.from_pandas(
gdf.drop(columns=['geometry']),
schema=pa.schema(fields[:-1]), # schema without geometry
preserve_index=False
)
# Convert string columns - make sure they are actually strings (handle None -> null)
# The from_pandas with schema should handle the type casting
# Now add geometry column as WKB
from shapely import to_wkb
geo_col = pa.array(
[to_wkb(g) for g in gdf['geometry']],
type=pa.binary()
)
table = table.append_column('geometry', geo_col)
# Write GeoParquet
pq.write_table(
table,
'/work/addresses.geoparquet',
schema=schema,
compression='zstd',
row_group_size=1000
)
print("Done! Written /work/addresses.geoparquet")
# Verify
import pyarrow.parquet as pq
meta = pq.read_schema('/work/addresses.geoparquet')
print("Schema:")
print(meta)
# Quick test queries
import duckdb
con = duckdb.connect()
result = con.execute("""
SELECT COUNT(*) as cnt
FROM '/work/addresses.geoparquet'
WHERE recorded_at > '2024-01-01'
""").fetchone()
print(f"Rows with recorded_at > '2024-01-01': {result[0]}")
result2 = con.execute("""
SELECT SUM(unit_count) as total_units
FROM '/work/addresses.geoparquet'
""").fetchone()
print(f"Sum of unit_count: {result2[0]}")Wrote /work/solve.py (2360 chars).
1:06
Let me run the script and see if it works.
1:06
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 41, in <module>
table = pa.Table.from_pandas(
gdf.drop(columns=['geometry']),
schema=pa.schema(fields[:-1]), # schema without geometry
preserve_index=False
)
File "pyarrow/table.pxi", line 4768, in pyarrow.lib.Table.from_pandas
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 658, in dataframe_to_arrays
arrays.append(convert_column(c, f))
~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 639, in convert_column
raise e
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 633, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
File "pyarrow/array.pxi", line 390, in pyarrow.lib.array
File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowTypeError: ('Expected a string or bytes dtype, got int64', 'Conversion failed for column number with type int64')
1:13
The issue is that some columns like "number" might be numeric (int64) in the CSV. I need to convert all non-geometry, non-recorded_at, non-unit_count columns to string explicitly before building the table. Let me fix the script to cast all "other" columns to string in pandas first.
1:13
Edit
/work/solve.py
Edited /work/solve.py.
1:18
Wait, there's also an issue with the `number` column - it's int64 in pandas. Let me also handle the `recorded_at` column properly - it should already be a string but let me make sure. Actually, looking at the CSV data: - `id` - UUID string - `country` - "US" string - `postcode` - string like "10002" - `street` - string - `number` - numeric like "37", "45" - this will be read as int64 - `unit` - empty string or "1/2", "A" - `postal_city` - empty string - `recorded_at` - timestamp string - `unit_count` - integer - `geometry_wkt` - WKT string So after dropping geometry_wkt, the columns are: id, country, postcode, street, number, unit, postal_city, recorded_at, unit_count, geometry The issue is that `number` column is being read as int64 by pandas. I need to ensure it's treated as string. Let me fix the script to read all non-geometry, non-timestamp, non-int columns as string.
1:18
Edit
/work/solve.py
Edited /work/solve.py.
1:20
Actually, let me rethink the approach. The issue is that `number` column has integer values in some rows, so pandas reads it as int64. I need to handle this more carefully. Let me also check what pandas types we have.
1:20
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
df = pd.read_csv('/work/nyc_addresses.csv')
print(df.dtypes)
print('---')
print(df['unit_count'].head())
print('---')
print(df['number'].head())
"
id str country str postcode float64 street str number int64 unit str postal_city float64 recorded_at str unit_count int64 geometry_wkt str dtype: object --- 0 0 1 0 2 0 3 0 4 1 Name: unit_count, dtype: int64 --- 0 37 1 45 2 44 3 11 4 340 Name: number, dtype: int64
1:32
So the issue is that `postcode` is float64 (because of empty values in CSV), `number` is int64, `postal_city` is float64. These need to be converted to strings. Also `unit_count` is int64, which we need as int32. Let me rewrite the script more carefully.
1:32
Write
/work/solve.py
content · 95 lines · py
import geopandas as gpd
import pandas as pd
from shapely import wkt, to_wkb
import pyarrow as pa
import pyarrow.parquet as pq
# Read CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
df = df.drop(columns=['geometry_wkt'])
# Create GeoDataFrame with CRS EPSG:4326
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')
# Convert all columns to proper types
# string columns: id, country, postcode, street, number, unit, postal_city
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
for col in string_cols:
gdf[col] = gdf[col].astype(str)
# Replace 'nan', 'NaN', '' with None
gdf[col] = gdf[col].replace(['nan', 'NaN', ''], None)
# recorded_at as timestamp - parse to datetime
gdf['recorded_at'] = pd.to_datetime(gdf['recorded_at'])
# unit_count as int32
gdf['unit_count'] = gdf['unit_count'].astype('int32')
# Build pyarrow schema
fields = [
pa.field('id', pa.string()),
pa.field('country', pa.string()),
pa.field('postcode', pa.string()),
pa.field('street', pa.string()),
pa.field('number', pa.string()),
pa.field('unit', pa.string()),
pa.field('postal_city', pa.string()),
pa.field('recorded_at', pa.timestamp('us')),
pa.field('unit_count', pa.int32()),
pa.field('geometry', pa.binary()),
]
schema = pa.schema(fields)
# Build the pyarrow table manually to have full control
cols = []
for col in ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']:
cols.append(pa.array(gdf[col].tolist(), type=pa.string()))
cols.append(pa.array(gdf['recorded_at'].tolist(), type=pa.timestamp('us')))
cols.append(pa.array(gdf['unit_count'].tolist(), type=pa.int32()))
cols.append(pa.array([to_wkb(g) for g in gdf['geometry']], type=pa.binary()))
table = pa.table(dict(zip([f.name for f in schema], cols)), schema=schema)
# Write GeoParquet
pq.write_table(
table,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000
)
print("Done! Written /work/addresses.geoparquet")
# Verify schema
meta = pq.read_schema('/work/addresses.geoparquet')
print("Schema:")
print(meta)
# Quick test queries with DuckDB
import duckdb
con = duckdb.connect()
result = con.execute("""
SELECT COUNT(*) as cnt
FROM '/work/addresses.geoparquet'
WHERE recorded_at > '2024-01-01'
""").fetchone()
print(f"Rows with recorded_at > '2024-01-01': {result[0]}")
result2 = con.execute("""
SELECT SUM(unit_count) as total_units
FROM '/work/addresses.geoparquet'
""").fetchone()
print(f"Sum of unit_count: {result2[0]}")
# Also verify that column types allow direct access without CAST
result3 = con.execute("""
SELECT column_name, data_type
FROM information_schema.columns
WHERE table_name = 'addresses.geoparquet' OR table_name = 'addresses'
ORDER BY column_name
""").fetchall()
for r in result3:
print(f" {r[0]}: {r[1]}")Wrote /work/solve.py (2958 chars).
1:35
Wait, `unit_count` in the CSV was read as int64 by pandas, but the column `number` was also int64. I need to convert `number` to string as well. Let me check my script - I already have `number` in the string_cols list, so that's fine. Let me run it.
1:35
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 49, in <module>
cols.append(pa.array(gdf[col].tolist(), type=pa.string()))
~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 400, in pyarrow.lib.array
File "pyarrow/array.pxi", line 46, in pyarrow.lib._sequence_to_array
chunked = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Expected bytes, got a 'float' object
1:39
The issue is that when we convert to string, pandas stores None as NaN (float). Let me handle this more carefully.
1:39
Edit
/work/solve.py
Edited /work/solve.py.
1:40
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 56, in <module>
cols.append(pa.array(gdf[col].tolist(), type=pa.string()))
~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 400, in pyarrow.lib.array
File "pyarrow/array.pxi", line 46, in pyarrow.lib._sequence_to_array
chunked = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Expected bytes, got a 'float' object
1:45
The issue is that some values might be floats (NaN) that are not being caught properly. Let me debug which column is causing the issue.
1:45
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
import numpy as np
df = pd.read_csv('/work/nyc_addresses.csv')
# Check which columns have non-string types
for col in ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']:
print(f'{col}: {df[col].dtype}, unique types: {set(type(v).__name__ for v in df[col])}')
# Check for NaN
print(f' NaN count: {df[col].isna().sum()}')
# Check sample values
print(f' samples: {df[col].dropna().unique()[:3].tolist()}')
print()
# Check number column specifically
print('number values:', df['number'].head(10).tolist())
print('number dtype:', df['number'].dtype)
"
id: str, unique types: {'str'}
NaN count: 0
samples: ['0005eda0-2540-4e36-bc81-dfb52e323d99', '00587e13-6253-485d-9258-e47d43af7559', '0070f126-d36a-43b3-b767-a8fbb7156602']
country: str, unique types: {'str'}
NaN count: 0
samples: ['US']
postcode: float64, unique types: {'float'}
NaN count: 1
samples: [10002.0, 10038.0, 11201.0]
street: str, unique types: {'str'}
NaN count: 0
samples: ['ALLEN ST', 'MADISON ST', 'PECK SLIP']
number: int64, unique types: {'int'}
NaN count: 0
samples: [37, 45, 44]
unit: str, unique types: {'str', 'float'}
NaN count: 992
samples: ['1/2', 'A', 'E']
postal_city: float64, unique types: {'float'}
NaN count: 1056
samples: []
number values: [37, 45, 44, 11, 340, 299, 37, 184, 53, 1]
number dtype: int64
1:58
The issue is that `number` column is `int64` in pandas, and when I do `apply(clean_to_str)`, the `pd.isna()` check passes fine but `str(val)` returns e.g. `"37"` which is fine. But the error says `Expected bytes, got a 'float' object`. Let me check what `gdf['postcode'].tolist()` looks like.
Wait, the error is about 'float' object. Let me check more carefully. `postcode` is float64, so NaN values are float NaN. After my `clean_to_str`, `pd.isna(float('nan'))` returns True, so NaN would be converted to None. But what about the non-NaN values? `str(10002.0)` gives `"10002.0"`. But `pa.array([...], type=pa.string())` should accept strings...
Actually, wait - the error says `Expected bytes, got a 'float' object`. So somewhere a float is sneaking through. Let me check - if `pd.isna(None)` → True, so `None` values become None. But when I call `.tolist()` on a pandas Series with None values, the None becomes `float('nan')` or stays as None?
Actually, when you have a pandas object column with None values, `.tolist()` returns a list with `None` entries. But when the column dtype is `float64`, NaN values stay as `float('nan')`.
For `postcode`, the dtype is `float64`. When I apply `clean_to_str`, `pd.isna(np.nan)` is True, so it returns `None`. But then the column type might be `object` with some None values... Let me think.
Actually, the issue might be with the `number` column. It's `int64`. When I do `gdf[col].apply(clean_to_str)`, `str(37)` = `"37"`, which is fine. But then `.tolist()` gives a list of strings. That should work fine with `pa.array(..., type=pa.string())`.
Wait, the error says `Expected bytes, got a 'float' object`. Let me trace back. The column 'postcode' has float64 dtype. After `apply(clean_to_str)`, it becomes object dtype with values like "10002.0", "10038.0", etc. Those are strings. `.tolist()` should give strings... unless some NaN values slip through.
Actually, I need to check if `pd.isna` catches all cases. For `postcode`, there's 1 NaN. `pd.isna(float('nan'))` is True. So `clean_to_str(float('nan'))` returns `None`. Good. But what about `float('nan')` in general? `pd.isna(float('nan'))` is True, returns None. So that should be fine.
Let me check: does `.apply` preserve None vs NaN? When you return None from apply, it stays as None in the Series of object dtype. Good.
Hmm, let me just run a small test to reproduce the error.
Actually, the error might be from the `postcode` column. Values like `10002.0` converted to string `"10002.0"`. But pa.array() with type=pa.string() should accept Python strings...
Let me try a different approach - use pandas string type `'string'` (pd.StringDtype()) which handles NA properly, or just convert to string before writing.
Let me try a simpler approach: convert all to string using pandas nullable string type.
1:58
1:58
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
import numpy as np
df = pd.read_csv('/work/nyc_addresses.csv')
# Check the actual data types after our cleanup
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
def clean_to_str(val):
if pd.isna(val):
return None
s = str(val)
if s in ('nan', 'NaN', ''):
return None
return s
for col in string_cols:
series = df[col].apply(clean_to_str)
print(f'{col}: dtype={series.dtype}, sample={series.head(5).tolist()}')
# Check for non-string types in the list
types_in_list = set(type(v).__name__ for v in series.tolist() if v is not None)
print(f' non-None types: {types_in_list}')
"
id: dtype=str, sample=['0005eda0-2540-4e36-bc81-dfb52e323d99', '00587e13-6253-485d-9258-e47d43af7559', '0070f126-d36a-43b3-b767-a8fbb7156602', '00a8fc38-5d9a-42da-94a9-7d2f4101f82c', '00f815af-51f6-44c2-b0d6-ee23092d14b2']
non-None types: {'str'}
country: dtype=str, sample=['US', 'US', 'US', 'US', 'US']
non-None types: {'str'}
postcode: dtype=str, sample=['10002.0', '10038.0', '10038.0', '11201.0', '10280.0']
non-None types: {'float', 'str'}
street: dtype=str, sample=['ALLEN ST', 'MADISON ST', 'PECK SLIP', 'FRONT ST', 'ALBANY ST']
non-None types: {'str'}
number: dtype=str, sample=['37', '45', '44', '11', '340']
non-None types: {'str'}
unit: dtype=str, sample=['1/2', nan, nan, nan, nan]
non-None types: {'float', 'str'}
postal_city: dtype=object, sample=[None, None, None, None, None]
non-None types: set()
2:05
The issue is that `postcode` has `float` values in the list (because `10002.0` converts to the float `10002.0` not a string). When I do `.tolist()`, pandas may return some items as actual float objects. Let me check more carefully.
Wait, the `Non-None types` for postcode shows `{'float', 'str'}` - that means some values are still float objects. This is because when the value is `nan` (from pandas), after `pd.isna(val)` returns True and the function returns None. But wait, it says non-None types includes 'float'. Let me check what's happening.
Actually, the issue might be that `10002.0` when converted to string becomes `'10002.0'` but when we do `.tolist()` the numpy/pandas might convert it back. Let me check:
Actually, after `.apply(clean_to_str)`, the series has dtype 'str' for postcode. But .tolist() converts to a Python list where numpy floats might show up. Let's check.
2:05
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
import numpy as np
df = pd.read_csv('/work/nyc_addresses.csv')
def clean_to_str(val):
if pd.isna(val):
return None
s = str(val)
if s in ('nan', 'NaN', ''):
return None
return s
col = 'postcode'
series = df[col].apply(clean_to_str)
vals = series.tolist()
for i, v in enumerate(vals[:10]):
print(f' [{i}] {repr(v)} type={type(v).__name__}')
"
[0] '10002.0' type=str [1] '10038.0' type=str [2] '10038.0' type=str [3] '11201.0' type=str [4] '10280.0' type=str [5] '10013.0' type=str [6] '10013.0' type=str [7] '10012.0' type=str [8] '10002.0' type=str [9] '10007.0' type=str
2:10
The issue is that `pd.isna()` on string "nan" returns False, so the string "nan" stays as a Python string. But wait, the error says "Expected bytes, got a 'float' object". Let me look more carefully. Let me check the whole tolist() result more carefully - perhaps there are some NaN float values in the series that aren't being caught.
2:10
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
import numpy as np
df = pd.read_csv('/work/nyc_addresses.csv')
def clean_to_str(val):
if pd.isna(val):
return None
s = str(val)
if s in ('nan', 'NaN', ''):
return None
return s
# Check for problematic values
col = 'unit'
vals = df[col].apply(clean_to_str).tolist()
# Find non-string, non-None values
for i, v in enumerate(vals):
if v is not None and not isinstance(v, str):
print(f'unit[{i}]: type={type(v).__name__}, value={repr(v)}')
# Find where postcode has float
col = 'postcode'
vals = df[col].apply(clean_to_str).tolist()
for i, v in enumerate(vals):
if v is not None and not isinstance(v, str):
print(f'postcode[{i}]: type={type(v).__name__}, value={repr(v)}')
"
unit[1]: type=float, value=nan unit[2]: type=float, value=nan unit[3]: type=float, value=nan unit[4]: type=float, value=nan unit[5]: type=float, value=nan unit[6]: type=float, value=nan unit[7]: type=float, value=nan unit[8]: type=float, value=nan unit[9]: type=float, value=nan unit[10]: type=float, value=nan unit[11]: type=float, value=nan unit[12]: type=float, value=nan unit[13]: type=float, value=nan unit[14]: type=float, value=nan unit[15]: type=float, value=nan unit[16]: type=float, value=nan unit[17]: type=float, value=nan unit[18]: type=float, value=nan unit[20]: type=float, value=nan unit[21]: type=float, value=nan unit[22]: type=float, value=nan unit[23]: type=float, value=nan unit[24]: type=float, value=nan unit[25]: type=float, value=nan unit[26]: type=float, value=nan unit[27]: type=float, value=nan unit[28]: type=float, value=nan unit[29]: type=float, value=nan unit[30]: type=float, value=nan unit[31]: type=float, value=nan unit[32]: type=float, value=nan unit[33]: type=float, value=nan unit[34]: type=float, value=nan unit[35]: type=float, value=nan unit[36]: type=float, value=nan unit[37]: type=float, value=nan unit[38]: type=float, value=nan unit[39]: type=float, value=nan unit[40]: type=float, value=nan unit[41]: type=float, value=nan unit[42]: type=float, value=nan unit[43]: type=float, value=nan unit[44]: type=float, value=nan unit[45]: type=float, value=nan unit[46]: type=float, value=nan unit[47]: type=float, value=nan unit[48]: type=float, value=nan unit[49]: type=float, value=nan unit[50]: type=float, value=nan unit[51]: type=float, value=nan unit[52]: type=float, value=nan unit[53]: type=float, value=nan unit[54]: type=float, value=nan unit[55]: type=float, value=nan unit[56]: type=float, value=nan unit[57]: type=float, value=nan unit[58]: type=float, value=nan unit[59]: type=float, value=nan unit[60]: type=float, value=nan unit[61]: type=float, value=nan unit[62]: type=float, value=nan unit[64]: type=float, value=nan unit[66]: type=float, value=nan unit[68]: type=float, value=nan unit[69]: type=float, value=nan unit[70]: type=float, value=nan unit[71]: type=float, value=nan unit[72]: type=float, value=nan unit[73]: type=float, value=nan unit[74]: type=float, value=nan unit[75]: type=float, value=nan unit[76]: type=float, value=nan unit[77]: type=float, value=nan unit[78]: type=float, value=nan unit[79]: type=float, value=nan unit[81]: type=float, value=nan unit[82]: type=float, value=nan unit[83]: type=float, value=nan unit[84]: type=float, value=nan unit[85]: type=float, value=nan unit[86]: type=float, value=nan unit[87]: type=float, value=nan unit[88]: type=float, value=nan unit[89]: type=float, value=nan unit[90]: type=float, value=nan unit[91]: type=float, value=nan unit[92]: type=float, value=nan unit[93]: type=float, value=nan unit[94]: type=float, value=nan unit[95]: type=float, value=nan unit[96]: type=float, value=nan unit[97]: type=float, value=nan unit[98]: type=float, value=nan unit[99]: type=float, value=nan unit[100]: type=float, value=nan unit[101]: type=float, value=nan unit[103]: type=float, value=nan unit[104]: type=float, value=nan unit[106]: type=float, value=nan unit[107]: type=float, value=nan unit[108]: type=float, value=nan unit[109]: type=float, value=nan unit[110]: type=float, value=nan unit[111]: type=float, value=nan unit[112]: type=float, value=nan unit[113]: type=float, value=nan unit[114]: type=float, value=nan unit[115]: type=float, value=nan unit[116]: type=float, value=nan unit[117]: type=float, value=nan unit[118]: type=float, value=nan unit[119]: type=float, value=nan unit[120]: type=float, value=nan unit[121]: type=float, value=nan unit[122]: type=float, value=nan unit[123]: type=float, value=nan unit[124]: type=float, value=nan unit[125]: type=float, value=nan unit[126]: type=float, value=nan unit[127]: type=float, value=nan unit[128]: type=float, value=nan unit[130]: type=float, value=nan unit[131]: type=float, value=nan unit[132]: type=float, value=nan unit[133]: type=float, value=nan unit[134]: type=float, value=nan unit[135]: type=float, value=nan unit[136]: type=float, value=nan unit[137]: type=float, value=nan unit[138]: type=float, value=nan unit[139]: type=float, value=nan unit[140]: type=float, value=nan unit[141]: type=float, value=nan unit[142]: type=float, value=nan unit[144]: type=float, value=nan unit[145]: type=float, value=nan unit[146]: type=float, value=nan unit[147]: type=float, value=nan unit[148]: type=float, value=nan unit[149]: type=float, value=nan unit[150]: type=float, value=nan unit[151]: type=float, value=nan unit[152]: type=float, value=nan unit[153]: type=float, value=nan unit[154]: type=float, value=nan unit[155]: type=float, value=nan unit[156]: type=float, value=nan unit[157]: type=float, value=nan unit[158]: type=float, value=nan unit[159]: type=float, value=nan unit[160]: type=float, value=nan unit[161]: type=float, value=nan unit[162]: type=float, value=nan unit[163]: type=float, value=nan unit[164]: type=float, value=nan unit[165]: type=float, value=nan unit[166]: type=float, value=nan unit[167]: type=float, value=nan unit[168]: type=float, value=nan unit[169]: type=float, value=nan unit[170]: type=float, value=nan unit[171]: type=float, value=nan unit[173]: type=float, value=nan unit[177]: type=float, value=nan unit[178]: type=float, value=nan unit[179]: type=float, value=nan unit[180]: type=float, value=nan unit[181]: type=float, value=nan unit[182]: type=float, value=nan unit[183]: type=float, value=nan unit[186]: type=float, value=nan unit[188]: type=float, value=nan unit[189]: type=float, value=nan unit[190]: type=float, value=nan unit[191]: type=float, value=nan unit[193]: type=float, value=nan unit[194]: type=float, value=nan unit[195]: type=float, value=nan unit[196]: type=float, value=nan unit[197]: type=float, value=nan unit[198]: type=float, value=nan unit[199]: type=float, value=nan unit[200]: type=float, value=nan unit[201]: type=float, value=nan unit[202]: type=float, value=nan unit[203]: type=float, value=nan unit[204]: type=float, value=nan unit[205]: type=float, value=nan unit[206]: type=float, value=nan unit[207]: type=float, value=nan unit[208]: type=float, value=nan unit[209]: type=float, value=nan unit[210]: type=float, value=nan unit[211]: type=float, value=nan unit[212]: type=float, value=nan unit[213]: type=float, value=nan unit[214]: type=float, value=nan unit[215]: type=float, value=nan unit[216]: type=float, value=nan unit[217]: type=float, value=nan unit[218]: type=float, value=nan unit[219]: type=float, value=nan unit[220]: type=float, value=nan unit[221]: type=float, value=nan unit[222]: type=float, value=nan unit[223]: type=float, value=nan unit[225]: type=float, value=nan unit[226]: type=float, value=nan unit[227]: type=float, value=nan unit[228]: type=float, value=nan unit[229]: type=float, value=nan unit[230]: type=float, value=nan unit[231]: type=float, value=nan unit[232]: type=float, value=nan unit[233]: type=float, value=nan unit[234]: type=float, value=nan unit[235]: type=float, value=nan unit[236]: type=float, value=nan unit[237]: type=float, value=nan unit[238]: type=float, value=nan unit[239]: type=float, value=nan unit[240]: type=float, value=nan unit[241]: type=float, value=nan unit[242]: type=float, value=nan unit[243]: type=float, value=nan unit[244]: type=float, value=nan unit[246]: type=float, value=nan unit[247]: type=float, value=nan unit[248]: type=float, value=nan unit[249]: type=float, value=nan unit[250]: type=float, value=nan unit[251]: type=float, value=nan unit[252]: type=float, value=nan unit[253]: type=float, value=nan unit[254]: type=float, value=nan unit[255]: type=float, value=nan unit[256]: type=float, value=nan unit[257]: type=float, value=nan unit[258]: type=float, value=nan unit[259]: type=float, value=nan unit[260]: type=float, value=nan unit[261]: type=float, value=nan unit[262]: type=float, value=nan unit[263]: type=float, value=nan unit[264]: type=float, value=nan unit[265]: type=float, value=nan unit[266]: type=float, value=nan unit[267]: type=float, value=nan unit[268]: type=float, value=nan unit[269]: type=float, value=nan unit[270]: type=float, value=nan unit[271]: type=float, value=nan unit[272]: type=float, value=nan unit[273]: type=float, value=nan unit[274]: type=float, value=nan unit[275]: type=float, value=nan unit[276]: type=float, value=nan unit[277]: type=float, value=nan unit[278]: type=float, value=nan unit[279]: type=float, value=nan unit[280]: type=float, value=nan unit[281]: type=float, value=nan unit[282]: type=float, value=nan unit[283]: type=float, value=nan unit[284]: type=float, value=nan unit[285]: type=float, value=nan unit[286]: type=float, value=nan unit[287]: type=float, value=nan unit[288]: type=float, value=nan unit[289]: type=float, value=nan unit[290]: type=float, value=nan unit[291]: type=float, value=nan unit[292]: type=float, value=nan unit[293]: type=float, value=nan unit[294]: type=float, value=nan unit[295]: type=float, value=nan unit[296]: type=float, value=nan unit[297]: type=float, value=nan unit[298]: type=float, value=nan unit[299]: type=float, value=nan unit[300]: type=float, value=nan unit[301]: type=float, value=nan unit[302]: type=float, value=nan unit[303]: type=float, value=nan unit[304]: type=float, value=nan unit[305]: type=float, value=nan unit[306]: type=float, value=nan unit[307]: type=float, value=nan unit[308]: type=float, value=nan unit[309]: type=float, value=nan unit[310]: type=float, value=nan unit[311]: type=float, value=nan unit[312]: type=float, value=nan unit[313]: type=float, value=nan unit[314]: type=float, value=nan unit[315]: type=float, value=nan unit[316]: type=float, value=nan unit[317]: type=float, value=nan unit[319]: type=float, value=nan unit[320]: type=float, value=nan unit[321]: type=float, value=nan unit[322]: type=float, value=nan unit[323]: type=float, value=nan unit[324]: type=float, value=nan unit[325]: type=float, value=nan unit[326]: type=float, value=nan unit[327]: type=float, value=nan unit[328]: type=float, value=nan unit[329]: type=float, value=nan unit[330]: type=float, value=nan unit[331]: type=float, value=nan unit[332]: type=float, value=nan unit[333]: type=float, value=nan unit[334]: type=float, value=nan unit[335]: type=float, value=nan unit[336]: type=float, value=nan unit[338]: type=float, value=nan unit[339]: type=float, value=nan unit[340]: type=float, value=nan unit[341]: type=float, value=nan unit[342]: type=float, value=nan unit[343]: type=float, value=nan unit[344]: type=float, value=nan unit[345]: type=float, value=nan unit[346]: type=float, value=nan unit[347]: type=float, value=nan unit[348]: type=float, value=nan unit[349]: type=float, value=nan unit[350]: type=float, value=nan unit[351]: type=float, value=nan unit[352]: type=float, value=nan unit[353]: type=float, value=nan unit[354]: type=float, value=nan unit[355]: type=float, value=nan unit[356]: type=float, value=nan unit[357]: type=float, value=nan unit[358]: type=float, value=nan unit[359]: type=float, value=nan unit[360]: type=float, value=nan unit[361]: type=float, value=nan unit[362]: type=float, value=nan unit[363]: type=float, value=nan unit[364]: type=float, value=nan unit[365]: type=float, value=nan unit[366]: type=float, value=nan unit[367]: type=float, value=nan unit[368]: type=float, value=nan unit[369]: type=float, value=nan unit[370]: type=float, value=nan unit[371]: type=float, value=nan unit[372]: type=float, value=nan unit[373]: type=float, value=nan unit[374]: type=float, value=nan unit[375]: type=float, value=nan unit[377]: type=float, value=nan unit[378]: type=float, value=nan unit[379]: type=float, value=nan unit[380]: type=float, value=nan unit[381]: type=float, value=nan unit[382]: type=float, value=nan unit[383]: type=float, value=nan unit[384]: type=float, value=nan unit[385]: type=float, value=nan unit[386]: type=float, value=nan unit[387]: type=float, value=nan unit[388]: type=float, value=nan unit[389]: type=float, value=nan unit[390]: type=float, value=nan unit[391]: type=float, value=nan unit[392]: type=float, value=nan unit[393]: type=float, value=nan unit[394]: type=float, value=nan unit[395]: type=float, value=nan unit[397]: type=float, value=nan unit[398]: type=float, value=nan unit[399]: type=float, value=nan unit[400]: type=float, value=nan unit[401]: type=float, value=nan unit[402]: type=float, value=nan unit[403]: type=float, value=nan unit[404]: type=float, value=nan unit[405]: type=float, value=nan unit[406]: type=float, value=nan unit[407]: type=float, value=nan unit[409]: type=float, value=nan unit[410]: type=float, value=nan unit[411]: type=float, value=nan unit[412]: type=float, value=nan unit[413]: type=float, value=nan unit[414]: type=float, value=nan unit[415]: type=float, value=nan unit[416]: type=float, value=nan unit[417]: type=float, value=nan unit[418]: type=float, value=nan unit[419]: type=float, value=nan unit[421]: type=float, value=nan unit[422]: type=float, value=nan unit[423]: type=float, value=nan unit[424]: type=float, value=nan unit[425]: type=float, value=nan unit[426]: type=float, value=nan unit[427]: type=float, value=nan unit[428]: type=float, value=nan unit[429]: type=float, value=nan unit[431]: type=float, value=nan unit[432]: type=float, value=nan unit[433]: type=float, value=nan unit[434]: type=float, value=nan unit[435]: type=float, value=nan unit[436]: type=float, value=nan unit[437]: type=float, value=nan unit[438]: type=float, value=nan unit[439]: type=float, value=nan unit[440]: type=float, value=nan unit[441]: type=float, value=nan unit[442]: type=float, value=nan unit[443]: type=float, value=nan unit[444]: type=float, value=nan unit[446]: type=float, value=nan unit[447]: type=float, value=nan unit[448]: type=float, value=nan unit[449]: type=float, value=nan unit[450]: type=float, value=nan unit[451]: 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type=float, value=nan unit[485]: type=float, value=nan unit[486]: type=float, value=nan unit[487]: type=float, value=nan unit[488]: type=float, [... truncated ...]0]: type=float, value=nan unit[581]: type=float, value=nan unit[582]: type=float, value=nan unit[583]: type=float, value=nan unit[584]: type=float, value=nan unit[585]: type=float, value=nan unit[586]: type=float, value=nan unit[587]: type=float, value=nan unit[588]: type=float, value=nan unit[589]: type=float, value=nan unit[590]: type=float, value=nan unit[591]: type=float, value=nan unit[592]: type=float, value=nan unit[593]: type=float, value=nan unit[594]: type=float, value=nan unit[595]: type=float, value=nan unit[596]: type=float, value=nan unit[597]: type=float, value=nan unit[598]: type=float, value=nan unit[599]: type=float, value=nan unit[600]: type=float, value=nan unit[601]: type=float, value=nan unit[602]: type=float, value=nan unit[603]: type=float, value=nan unit[604]: type=float, value=nan unit[605]: type=float, value=nan unit[606]: type=float, value=nan unit[607]: type=float, value=nan unit[608]: type=float, value=nan unit[609]: type=float, value=nan unit[610]: type=float, value=nan unit[611]: type=float, value=nan unit[612]: type=float, value=nan unit[613]: type=float, value=nan unit[614]: type=float, value=nan unit[615]: type=float, value=nan unit[616]: type=float, value=nan unit[617]: type=float, value=nan unit[618]: type=float, value=nan unit[619]: type=float, value=nan unit[620]: type=float, value=nan unit[622]: type=float, value=nan unit[623]: type=float, value=nan unit[624]: type=float, value=nan unit[625]: type=float, value=nan unit[626]: type=float, value=nan unit[627]: type=float, value=nan unit[628]: type=float, value=nan unit[629]: type=float, value=nan unit[630]: type=float, value=nan unit[632]: type=float, value=nan unit[633]: type=float, value=nan unit[634]: type=float, value=nan unit[635]: type=float, value=nan unit[636]: type=float, value=nan unit[637]: 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type=float, value=nan unit[670]: type=float, value=nan unit[671]: type=float, value=nan unit[672]: type=float, value=nan unit[673]: type=float, value=nan unit[674]: type=float, value=nan unit[675]: type=float, value=nan unit[676]: type=float, value=nan unit[678]: type=float, value=nan unit[679]: type=float, value=nan unit[680]: type=float, value=nan unit[681]: type=float, value=nan unit[682]: type=float, value=nan unit[683]: type=float, value=nan unit[684]: type=float, value=nan unit[685]: type=float, value=nan unit[686]: type=float, value=nan unit[687]: type=float, value=nan unit[688]: type=float, value=nan unit[689]: type=float, value=nan unit[691]: type=float, value=nan unit[692]: type=float, value=nan unit[693]: type=float, value=nan unit[694]: type=float, value=nan unit[695]: type=float, value=nan unit[696]: type=float, value=nan unit[697]: type=float, value=nan unit[698]: type=float, value=nan unit[700]: type=float, value=nan unit[701]: type=float, value=nan unit[702]: type=float, value=nan unit[703]: type=float, value=nan unit[704]: type=float, value=nan unit[705]: type=float, value=nan unit[706]: type=float, value=nan unit[707]: type=float, value=nan unit[708]: type=float, value=nan unit[710]: type=float, value=nan unit[711]: type=float, value=nan unit[713]: type=float, value=nan unit[714]: type=float, value=nan unit[715]: type=float, value=nan unit[716]: type=float, value=nan unit[717]: type=float, value=nan unit[718]: type=float, value=nan unit[720]: type=float, value=nan unit[721]: type=float, value=nan unit[722]: type=float, value=nan unit[723]: type=float, value=nan unit[724]: type=float, value=nan unit[725]: type=float, value=nan unit[726]: type=float, value=nan unit[727]: type=float, value=nan unit[728]: type=float, value=nan unit[729]: type=float, value=nan unit[730]: type=float, value=nan unit[731]: type=float, value=nan unit[732]: type=float, value=nan unit[733]: type=float, value=nan unit[734]: type=float, value=nan unit[735]: 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type=float, value=nan unit[988]: type=float, value=nan unit[989]: type=float, value=nan unit[990]: type=float, value=nan unit[991]: type=float, value=nan unit[992]: type=float, value=nan unit[993]: type=float, value=nan unit[994]: type=float, value=nan unit[995]: type=float, value=nan unit[996]: type=float, value=nan unit[997]: type=float, value=nan unit[998]: type=float, value=nan unit[999]: type=float, value=nan unit[1000]: type=float, value=nan unit[1001]: type=float, value=nan unit[1002]: type=float, value=nan unit[1003]: type=float, value=nan unit[1004]: type=float, value=nan unit[1005]: type=float, value=nan unit[1006]: type=float, value=nan unit[1007]: type=float, value=nan unit[1008]: type=float, value=nan unit[1009]: type=float, value=nan unit[1010]: type=float, value=nan unit[1011]: type=float, value=nan unit[1012]: type=float, value=nan unit[1013]: type=float, value=nan unit[1014]: type=float, value=nan unit[1015]: type=float, value=nan unit[1016]: type=float, value=nan unit[1017]: type=float, value=nan unit[1018]: type=float, value=nan unit[1019]: type=float, value=nan unit[1020]: type=float, value=nan unit[1022]: type=float, value=nan unit[1023]: type=float, value=nan unit[1024]: type=float, value=nan unit[1025]: type=float, value=nan unit[1026]: type=float, value=nan unit[1027]: type=float, value=nan unit[1028]: type=float, value=nan unit[1029]: type=float, value=nan unit[1030]: type=float, value=nan unit[1031]: type=float, value=nan unit[1032]: type=float, value=nan unit[1033]: type=float, value=nan unit[1034]: type=float, value=nan unit[1035]: type=float, value=nan unit[1036]: type=float, value=nan unit[1037]: type=float, value=nan unit[1038]: type=float, value=nan unit[1039]: type=float, value=nan unit[1040]: type=float, value=nan unit[1041]: type=float, value=nan unit[1042]: type=float, value=nan unit[1043]: type=float, value=nan unit[1044]: type=float, value=nan unit[1045]: type=float, value=nan unit[1046]: type=float, value=nan unit[1047]: type=float, value=nan unit[1048]: type=float, value=nan unit[1049]: type=float, value=nan unit[1050]: type=float, value=nan unit[1051]: type=float, value=nan unit[1053]: type=float, value=nan unit[1054]: type=float, value=nan unit[1055]: type=float, value=nan postcode[697]: type=float, value=nan
2:27
The issue is that `pd.isna()` returns True for `float('nan')` but the values after `apply()` are still `float('nan')` objects, not `None`. The `clean_to_str` function with `pd.isna(val)` doesn't catch float('nan') in some cases - actually it does, but the problem is that `pd.isna` returns True for NaN, but then we return `None`. However, looking at the output more carefully, the "unit" column has values like `nan` (as a Python float), and `pd.isna` should return True for those. Let me re-check.
Wait, the issue is different. Let me look at the error message: `ArrowTypeError: Expected bytes, got a 'float' object`. This means some value in a string column is still a float even after our cleaning. Looking at the output, the `postcode` column has `10002.0` (as string) in some entries but also might have some remaining float. Actually, the issue is that `pd.isna()` doesn't catch `float('nan')` in all cases.
Actually, looking more carefully at the output from the last debug run: `postcode[697]: type=float, value=nan` - this means the `clean_to_str` function returned `float('nan')` for that value. Let's check: `pd.isna(float('nan'))` should return True... Actually, let me check if maybe `pd.isna` works for float nan but the issue is that there's a `NaN` from numpy/pandas that passes through.
Let me just use a simpler approach: iterate through each value and use `None` replacement explicitly.
2:27
Write
/work/solve.py
content · 127 lines · py
import geopandas as pd
from shapely import wkt, to_wkb
import pyarrow as pa
import pyarrow.parquet as pq
import math
# Read CSV using pandas
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
df = df.drop(columns=['geometry_wkt'])
# Build a clean list of rows
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
# Convert to proper types using numpy arrays
import numpy as np
ids = []
countries = []
postcodes = []
streets = []
numbers = []
units = []
postal_cities = []
recorded_ats = []
unit_counts = []
geoms = []
for idx, row in df.iterrows():
# String columns: convert to None or string
for col in string_cols:
val = row[col]
if pd.isna(val) or val is None or val == '' or (isinstance(val, float) and math.isnan(val)):
val = None
else:
val = str(val)
if col == 'id':
ids.append(val)
elif col == 'country':
countries.append(val)
elif col == 'postcode':
postcodes.append(val)
elif col == 'street':
streets.append(val)
elif col == 'number':
numbers.append(val)
elif col == 'unit':
units.append(val)
elif col == 'postal_city':
postal_cities.append(val)
# recorded_at as datetime
recorded_ats.append(pd.Timestamp(row['recorded_at']))
# unit_count as int32
unit_counts.append(int(row['unit_count']))
# geometry as WKB
geoms.append(to_wkb(row['geometry']))
# Build pyarrow table manually
schema = pa.schema([
('id', pa.string()),
('country', pa.string()),
('postcode', pa.string()),
('street', pa.string()),
('number', pa.string()),
('unit', pa.string()),
('postal_city', pa.string()),
('recorded_at', pa.timestamp('us')),
('unit_count', pa.int32()),
('geometry', pa.binary()),
])
table = pa.table({
'id': pa.array(ids, type=pa.string()),
'country': pa.array(countries, type=pa.string()),
'postcode': pa.array(postcodes, type=pa.string()),
'street': pa.array(streets, type=pa.string()),
'number': pa.array(numbers, type=pa.string()),
'unit': pa.array(units, type=pa.string()),
'postal_city': pa.array(postal_cities, type=pa.string()),
'recorded_at': pa.array(recorded_ats, type=pa.timestamp('us')),
'unit_count': pa.array(unit_counts, type=pa.int32()),
'geometry': pa.array(geoms, type=pa.binary()),
}, schema=schema)
# Write GeoParquet
pq.write_table(
table,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000,
)
print("Done! Written /work/addresses.geoparquet")
# Verify schema
meta = pq.read_schema('/work/addresses.geoparquet')
print("Schema:")
print(meta)
# Quick test queries with DuckDB
import duckdb
con = duckdb.connect()
result = con.execute("""
SELECT COUNT(*) as cnt
FROM '/work/addresses.geoparquet'
WHERE recorded_at > '2024-01-01'
""").fetchone()
print(f"Rows with recorded_at > '2024-01-01': {result[0]}")
result2 = con.execute("""
SELECT SUM(unit_count) as total_units
FROM '/work/addresses.geoparquet'
""").fetchone()
print(f"Sum of unit_count: {result2[0]}")
# Also verify column types
result3 = con.execute("""
DESCRIBE '/work/addresses.geoparquet'
""").fetchall()
print("Column types:")
for r in result3:
print(f" {r[0]}: {r[1]}")Wrote /work/solve.py (3449 chars).
2:30
Wait, I imported geopandas as pd... That's wrong. Let me fix it - I need to import pandas properly.
2:30
Edit
/work/solve.py
Edited /work/solve.py.
2:32
Bash
cd /work && python solve.py
Done! Written /work/addresses.geoparquet Schema: id: string country: string postcode: string street: string number: string unit: string postal_city: string recorded_at: timestamp[us] unit_count: int32 geometry: binary
Traceback (most recent call last):
File "/work/solve.py", line 109, in <module>
result = con.execute("""
~~~~~~~~~~~^^^^
SELECT COUNT(*) as cnt
^^^^^^^^^^^^^^^^^^^^^^
FROM '/work/addresses.geoparquet'
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
WHERE recorded_at > '2024-01-01'
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
""").fetchone()
^^^^
_duckdb.BinderException: Binder Error: No extension found that is capable of reading the file "/work/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
2:40
The GeoParquet file was written but DuckDB can't read it directly. The issue might be that the geometry column is just `binary` type instead of the proper GeoParquet geometry encoding. Let me use `read_parquet` function in DuckDB, or better, use the proper GeoParquet metadata. Actually, the issue is that DuckDB needs to read it explicitly as parquet. Let me use `read_parquet` instead. But also, for proper GeoParquet, we need the geo metadata. Let me use `read_parquet` in DuckDB and also try using geopandas to verify.
2:40
Edit
/work/solve.py
Edited /work/solve.py.
2:43
Also need to remove the unused imports.
2:43
Edit
/work/solve.py
Edited /work/solve.py.
2:46
Bash
cd /work && python solve.py
Done! Written /work/addresses.geoparquet Schema: id: string country: string postcode: string street: string number: string unit: string postal_city: string recorded_at: timestamp[us] unit_count: int32 geometry: binary Rows with recorded_at > '2024-01-01': 1055 Sum of unit_count: 1566
Traceback (most recent call last):
File "/work/solve.py", line 122, in <module>
result3 = con.execute("""
~~~~~~~~~~~^^^^
DESCRIBE read_parquet('/work/addresses.geoparquet')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
""").fetchall()
^^^^
_duckdb.ParserException: Parser Error: syntax error at or near "("
LINE 2: DESCRIBE read_parquet('/work/addresses.geoparquet')
^
2:51
The GeoParquet file is created properly with the right schema, and the queries work. Let me fix the DESCRIBE query and also verify the GeoParquet is valid (has the geo metadata).
2:51
Edit
/work/solve.py
Edited /work/solve.py.
2:53
Ed
{
"file_path": "/work/solve.py"
}
Unknown tool: 'Ed'
2:55
I need to use Edit, not Ed. Let me just run the script as is.
2:55
Bash
cd /work && python solve.py
Done! Written /work/addresses.geoparquet Schema: id: string country: string postcode: string street: string number: string unit: string postal_city: string recorded_at: timestamp[us] unit_count: int32 geometry: binary Rows with recorded_at > '2024-01-01': 1055 Sum of unit_count: 1566 Column types: id: VARCHAR country: VARCHAR postcode: VARCHAR street: VARCHAR number: VARCHAR unit: VARCHAR postal_city: VARCHAR recorded_at: TIMESTAMP unit_count: INTEGER geometry: BLOB WARNING: No geo metadata found!
3:11
The GeoParquet lacks the geo metadata that tools like DuckDB spatial extension or GeoPandas need to recognize it as a spatial file. I need to add the geo metadata to the Parquet file's key-value metadata. Let me write it properly with the geo metadata.
3:11
3:11
Write
/work/solve.py
content · 220 lines · py
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from shapely import wkt, to_wkb
import math
import json
# Read CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
df = df.drop(columns=['geometry_wkt'])
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
# Build column arrays manually
ids = []
countries = []
postcodes = []
streets = []
numbers = []
units = []
postal_cities = []
recorded_ats = []
unit_counts = []
geoms = []
for idx, row in df.iterrows():
for col in string_cols:
val = row[col]
if pd.isna(val) or val is None or val == '' or (isinstance(val, float) and math.isnan(val)):
val = None
else:
val = str(val)
if col == 'id':
ids.append(val)
elif col == 'country':
countries.append(val)
elif col == 'postcode':
postcodes.append(val)
elif col == 'street':
streets.append(val)
elif col == 'number':
numbers.append(val)
elif col == 'unit':
units.append(val)
elif col == 'postal_city':
postal_cities.append(val)
recorded_ats.append(pd.Timestamp(row['recorded_at']))
unit_counts.append(int(row['unit_count']))
geoms.append(to_wkb(row['geometry']))
# Build schema
schema = pa.schema([
('id', pa.string()),
('country', pa.string()),
('postcode', pa.string()),
('street', pa.string()),
('number', pa.string()),
('unit', pa.string()),
('postal_city', pa.string()),
('recorded_at', pa.timestamp('us')),
('unit_count', pa.int32()),
('geometry', pa.binary()),
])
table = pa.table({
'id': pa.array(ids, type=pa.string()),
'country': pa.array(countries, type=pa.string()),
'postcode': pa.array(postcodes, type=pa.string()),
'street': pa.array(streets, type=pa.string()),
'number': pa.array(numbers, type=pa.string()),
'unit': pa.array(units, type=pa.string()),
'postal_city': pa.array(postal_cities, type=pa.string()),
'recorded_at': pa.array(recorded_ats, type=pa.timestamp('us')),
'unit_count': pa.array(unit_counts, type=pa.int32()),
'geometry': pa.array(geoms, type=pa.binary()),
}, schema=schema)
# Add GeoParquet metadata (v1.1.0 spec)
geo_metadata = {
"version": "1.1.0",
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"geometry_types": ["Point"],
"crs": {
"type": "GeographicCRS",
"properties": {
"name": "EPSG:4326"
}
},
"bbox": [
df['geometry'].x.min(),
df['geometry'].y.min(),
df['geometry'].x.max(),
df['geometry'].y.max()
]
}
}
}
# Write with geo metadata
pq.write_table(
table,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000,
metadata_collector=None,
)
# We need to write the metadata separately since write_table doesn't support arbitrary metadata easily
# Let's use the ParquetWriter approach
import pyarrow.parquet as pq
# Write first as regular parquet
pq.write_table(
table,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000,
)
# Now re-open and add geo metadata
pf = pq.ParquetFile('/work/addresses.geoparquet')
existing_meta = pf.metadata.metadata or {}
# Add geo metadata
geo_bytes = json.dumps(geo_metadata).encode('utf-8')
existing_meta[b'geo'] = geo_bytes
# Create new file metadata with the geo key
import pyarrow.parquet as pq
from pyarrow.parquet import ParquetFile, FileMetaData
# Read existing metadata
old_meta = pf.metadata
# Create new metadata with geo key
kvm = old_meta.metadata
kvm[b'geo'] = geo_bytes
# We can't easily modify metadata in place, so let's use a different approach:
# Write with write_metadata
# Actually let's just use pq.write_table with metadata parameter
# Better approach: write the geo metadata directly in the file
# Use pyarrow.parquet.write_metadata
from pyarrow import fs
# Let's just write the table and then manually update the metadata
# The simplest way is to use the Arrow ParquetWriter with key_value_metadata
# Let me use a clean approach
import pyarrow.parquet as pq
# Build key_value_metadata
kv_meta = pa.KeyValueMetadata({b'geo': geo_bytes})
# Write with the geo metadata attached to the schema
table_with_meta = table.replace_schema_metadata({
b'geo': geo_bytes
})
pq.write_table(
table_with_meta,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000,
)
print("Done! Written /work/addresses.geoparquet")
# Verify
pf = pq.ParquetFile('/work/addresses.geoparquet')
schema_out = pf.schema_arrow
print("Schema:")
print(schema_out)
# Check geo metadata
if b'geo' in pf.metadata.metadata:
geo_meta = json.loads(pf.metadata.metadata[b'geo'])
print(f"\nGeoParquet metadata present:")
print(json.dumps(geo_meta, indent=2))
else:
print("\nWARNING: No geo metadata found!")
# Quick tests with DuckDB
import duckdb
con = duckdb.connect()
# Install and load spatial extension for proper GeoParquet support
try:
con.execute("INSTALL spatial; LOAD spatial;")
except:
pass
result = con.execute("""
SELECT COUNT(*) as cnt
FROM read_parquet('/work/addresses.geoparquet')
WHERE recorded_at > '2024-01-01'
""").fetchone()
print(f"\nRows with recorded_at > '2024-01-01': {result[0]}")
result2 = con.execute("""
SELECT SUM(unit_count) as total_units
FROM read_parquet('/work/addresses.geoparquet')
""").fetchone()
print(f"Sum of unit_count: {result2[0]}")
# Verify types
result3 = con.execute("""
DESCRIBE SELECT * FROM read_parquet('/work/addresses.geoparquet')
""").fetchall()
print("\nColumn types:")
for r in result3:
print(f" {r[0]}: {r[1]}")Wrote /work/solve.py (5982 chars).
3:25
Let me clean up the script - there's a lot of redundancy. Let me simplify it.
3:25
Write
/work/solve.py
content · 159 lines · py
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from shapely import wkt, to_wkb
import math
import json
# Read CSV
df = pd.read_csv('/work/nyc_addresses.csv')
# Parse geometry from WKT
df['geometry'] = df['geometry_wkt'].apply(wkt.loads)
df = df.drop(columns=['geometry_wkt'])
string_cols = ['id', 'country', 'postcode', 'street', 'number', 'unit', 'postal_city']
# Build column arrays manually for full type control
ids = []
countries = []
postcodes = []
streets = []
numbers = []
units = []
postal_cities = []
recorded_ats = []
unit_counts = []
geoms = []
for idx, row in df.iterrows():
for col in string_cols:
val = row[col]
if pd.isna(val) or val is None or val == '' or (isinstance(val, float) and math.isnan(val)):
val = None
else:
val = str(val)
if col == 'id':
ids.append(val)
elif col == 'country':
countries.append(val)
elif col == 'postcode':
postcodes.append(val)
elif col == 'street':
streets.append(val)
elif col == 'number':
numbers.append(val)
elif col == 'unit':
units.append(val)
elif col == 'postal_city':
postal_cities.append(val)
recorded_ats.append(pd.Timestamp(row['recorded_at']))
unit_counts.append(int(row['unit_count']))
geoms.append(to_wkb(row['geometry']))
# Build schema
schema = pa.schema([
('id', pa.string()),
('country', pa.string()),
('postcode', pa.string()),
('street', pa.string()),
('number', pa.string()),
('unit', pa.string()),
('postal_city', pa.string()),
('recorded_at', pa.timestamp('us')),
('unit_count', pa.int32()),
('geometry', pa.binary()),
])
table = pa.table({
'id': pa.array(ids, type=pa.string()),
'country': pa.array(countries, type=pa.string()),
'postcode': pa.array(postcodes, type=pa.string()),
'street': pa.array(streets, type=pa.string()),
'number': pa.array(numbers, type=pa.string()),
'unit': pa.array(units, type=pa.string()),
'postal_city': pa.array(postal_cities, type=pa.string()),
'recorded_at': pa.array(recorded_ats, type=pa.timestamp('us')),
'unit_count': pa.array(unit_counts, type=pa.int32()),
'geometry': pa.array(geoms, type=pa.binary()),
}, schema=schema)
# GeoParquet metadata (v1.1.0 spec)
geo_metadata = {
"version": "1.1.0",
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"geometry_types": ["Point"],
"crs": {
"type": "GeographicCRS",
"properties": {
"name": "EPSG:4326"
}
},
"bbox": [
float(df['geometry'].x.min()),
float(df['geometry'].y.min()),
float(df['geometry'].x.max()),
float(df['geometry'].y.max())
]
}
}
}
geo_bytes = json.dumps(geo_metadata).encode('utf-8')
# Attach geo metadata to the table schema
table = table.replace_schema_metadata({b'geo': geo_bytes})
# Write GeoParquet
pq.write_table(
table,
'/work/addresses.geoparquet',
compression='zstd',
row_group_size=1000,
)
print("Done! Written /work/addresses.geoparquet")
# Verify schema
pf = pq.ParquetFile('/work/addresses.geoparquet')
print("Schema:")
print(pf.schema_arrow)
# Check geo metadata
if b'geo' in pf.metadata.metadata:
geo_meta = json.loads(pf.metadata.metadata[b'geo'])
print(f"\nGeoParquet metadata present:")
print(json.dumps(geo_meta, indent=2))
else:
print("\nWARNING: No geo metadata found!")
# Quick tests with DuckDB
import duckdb
con = duckdb.connect()
result = con.execute("""
SELECT COUNT(*) as cnt
FROM read_parquet('/work/addresses.geoparquet')
WHERE recorded_at > '2024-01-01'
""").fetchone()
print(f"\nRows with recorded_at > '2024-01-01': {result[0]}")
result2 = con.execute("""
SELECT SUM(unit_count) as total_units
FROM read_parquet('/work/addresses.geoparquet')
""").fetchone()
print(f"Sum of unit_count: {result2[0]}")
# Verify types
result3 = con.execute("""
DESCRIBE SELECT * FROM read_parquet('/work/addresses.geoparquet')
""").fetchall()
print("\nColumn types:")
for r in result3:
print(f" {r[0]}: {r[1]}")
print("\nAll checks passed!")Wrote /work/solve.py (4374 chars).
3:26
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 97, in <module>
float(df['geometry'].x.min()),
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'Series' object has no attribute 'x'. Did you mean: 'xs'?
3:31
The geometry column in the DataFrame is a Series of shapely Point objects, not a GeoDataFrame. I need to use `.x` attribute on each geometry.
3:31
Edit
/work/solve.py
Edited /work/solve.py.
3:32
Bash
cd /work && python solve.py
Done! Written /work/addresses.geoparquet
Schema:
id: string
country: string
postcode: string
street: string
number: string
unit: string
postal_city: string
recorded_at: timestamp[us]
unit_count: int32
geometry: binary
-- schema metadata --
geo: '{"version": "1.1.0", "primary_column": "geometry", "columns": {"geo' + 189
GeoParquet metadata present:
{
"version": "1.1.0",
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"geometry_types": [
"Point"
],
"crs": {
"type": "GeographicCRS",
"properties": {
"name": "EPSG:4326"
}
},
"bbox": [
-74.0182824,
40.7001627,
-73.9900156,
40.7299429
]
}
}
}
Rows with recorded_at > '2024-01-01': 1055
Sum of unit_count: 1566
Column types:
id: VARCHAR
country: VARCHAR
postcode: VARCHAR
street: VARCHAR
number: VARCHAR
unit: VARCHAR
postal_city: VARCHAR
recorded_at: TIMESTAMP
unit_count: INTEGER
geometry: GEOMETRY('{"type":"GeographicCRS","properties":{"name":"EPSG:4326"}}')
All checks passed!
3:38
Everything works perfectly. Let me verify the output file exists and summarize what was done. The queries work without CASTs: - `WHERE recorded_at > '2024-01-01'` works (returns 1055 rows) - `SUM(unit_count)` works (returns 1566) - DuckDB recognizes the geometry column as GEOMETRY type with CRS info The schema shows: - `recorded_at` as `timestamp[us]` (TIMESTAMP in DuckDB) - `unit_count` as `int32` (INTEGER in DuckDB) - All other columns as `string` (VARCHAR in DuckDB) - `geometry` as `binary` (WKB) with GeoParquet metadata
3:38