crs-l1-nyc-webmercator-cycleways
Map (reference ← swipe → agent)
0:00
The `nyc_cycleways` file came out of our tile-renderer, but our web map client can't read the coordinates as they are. It needs plain lat/lon. Can you convert them over? Please leave the attributes alone, and make sure each geometry stays a plain LineString rather than turning into a MultiLineString. Write the result as GeoParquet to `nyc_cycleways_wgs84.geoparquet`, and use `id` as the key.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:05
Let me start by examining the source file to understand its structure and coordinate system.
0:05
Bash
cd /work && ls -la nyc_cycleways*
-rw-r--r-- 1 agent agent 86058 Jun 16 21:46 nyc_cycleways_webmercator.geoparquet
0:10
Let me first explore the file to understand its structure.
0:10
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet')
print(gdf.head())
print(gdf.crs)
print(gdf.columns.tolist())
print(gdf.dtypes)
print(gdf.geometry.iloc[0])
print('---')
print(gdf.geometry.geom_type.unique())
"
Traceback (most recent call last):
File "<string>", line 3, in <module>
gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet')
File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 316, in _read_file
return _read_file_pyogrio(
filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
)
File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 576, in _read_file_pyogrio
return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py", line 382, in read_dataframe
result = read_func(
path_or_buffer,
...<15 lines>...
**kwargs,
)
File "/usr/local/lib/python3.14/site-packages/pyogrio/raw.py", line 200, in read
return ogr_read(
get_vsi_path_or_buffer(path_or_buffer),
...<15 lines>...
datetime_as_string=datetime_as_string,
)
File "pyogrio/_io.pyx", line 1412, in pyogrio._io.ogr_read
File "pyogrio/_io.pyx", line 257, in pyogrio._io.ogr_open
pyogrio.errors.DataSourceError: 'nyc_cycleways_webmercator.geoparquet' not recognized as being in a supported file format.; It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.
0:19
Let me check what files are in the directory and try using duckdb or pyarrow to read the parquet file.
0:19
Bash
cd /work && ls -la
total 88 drwxr-xr-x 1 agent agent 72 Jun 16 21:46 . drwxr-xr-x 1 root root 14 Jun 16 21:46 .. -rw-r--r-- 1 agent agent 86058 Jun 16 21:46 nyc_cycleways_webmercator.geoparquet
0:19
Bash
cd /work && python3 -c "
cd /work && python3 -c " import duckdb print(duckdb.sql(\"SELECT * FROM 'nyc_cycleways_webmercator.geoparquet' LIMIT 3\").fetchdf()) "
Traceback (most recent call last):
File "<string>", line 3, in <module>
print(duckdb.sql("SELECT * FROM 'nyc_cycleways_webmercator.geoparquet' LIMIT 3").fetchdf())
~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
_duckdb.BinderException: Binder Error: No extension found that is capable of reading the file "nyc_cycleways_webmercator.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
0:24
The file is named `.geoparquet` but it seems it might actually be a shapefile or something else. Let me check the file bytes to see what it actually is.
0:24
Bash
cd /work && file nyc_cycleways_webmercator.geoparquet
bash: line 1: file: command not found
0:24
Bash
cd /work && head -c 20 nyc_cycleways_webmercator.geoparquet | xxd
bash: line 1: xxd: command not found
0:36
Let me check the file more carefully. The file is called `.geoparquet` but maybe it's actually a GeoPackage? The error from pyogrio says it can't recognize the format. Let me try reading it with different drivers or checking it with Python.
0:36
Bash
cd /work && python3 -c "
cd /work && python3 -c " import pyogrio # Try to detect format print(pyogrio.list_drivers()) "
{'MEM': 'rw', 'PCIDSK': 'rw', 'PDS4': 'rw', 'VICAR': 'rw', 'PDF': 'rw', 'MBTiles': 'rw', 'EEDA': 'r', 'OGCAPI': 'r', 'GDALG': 'r', 'ESRI Shapefile': 'rw', 'MapInfo File': 'rw', 'LVBAG': 'r', 'S57': 'rw', 'DGN': 'rw', 'OGR_VRT': 'r', 'CSV': 'rw', 'GML': 'rw', 'GPX': 'rw', 'LIBKML': 'rw', 'KML': 'rw', 'GeoJSON': 'rw', 'GeoJSONSeq': 'rw', 'ESRIJSON': 'r', 'TopoJSON': 'r', 'OGR_GMT': 'rw', 'GPKG': 'rw', 'SQLite': 'rw', 'WAsP': 'rw', 'OpenFileGDB': 'rw', 'DXF': 'rw', 'FlatGeobuf': 'rw', 'GeoRSS': 'rw', 'VFK': 'r', 'PGDUMP': 'rw', 'OSM': 'r', 'GPSBabel': 'rw', 'OGR_PDS': 'r', 'WFS': 'r', 'OAPIF': 'r', 'EDIGEO': 'r', 'Idrisi': 'r', 'ODS': 'rw', 'XLSX': 'rw', 'Elasticsearch': 'rw', 'Carto': 'rw', 'AmigoCloud': 'rw', 'SXF': 'r', 'Selafin': 'rw', 'JML': 'rw', 'PLSCENES': 'r', 'CSW': 'r', 'VDV': 'rw', 'MVT': 'rw', 'NGW': 'rw', 'MapML': 'rw', 'GTFS': 'r', 'PMTiles': 'rw', 'JSONFG': 'rw', 'MiraMonVector': 'rw', 'ADBC': 'r', 'AVCBin': 'r', 'AVCE00': 'r', 'AIVector': 'r', 'HTTP': 'r'}
0:54
The file is named `.geoparquet` but pyogrio doesn't support GeoParquet natively. Let me check if it's actually a Parquet file by trying to read it with pandas/pyarrow or duckdb with parquet extension.
0:54
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pyarrow.parquet as pq
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
print(table.schema)
print(table.num_rows)
print(table.column_names)
"
id: string
class: string
subclass: string
name: string
geometry: binary
-- field metadata --
ARROW:extension:name: 'geoarrow.wkb'
ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 2460
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 813
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 2367
272
['id', 'class', 'subclass', 'name', 'geometry']
0:59
It's a GeoParquet file with geometry in WKB format and a Web Mercator CRS (EPSG:3857). I need to: 1. Read the GeoParquet file 2. Convert the geometries from Web Mercator (EPSG:3857) to WGS84 (EPSG:4326) 3. Make sure each geometry stays a LineString (not MultiLineString) 4. Write as GeoParquet with `id` as the key Let me use pyarrow and geoarrow/pyproj/shapely to do this properly.
0:59
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pyarrow.parquet as pq
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
print('Schema:')
print(table.schema)
print()
# Check geo metadata
import json
md = table.schema.metadata
print('Metadata keys:', list(md.keys()))
geo_md = json.loads(md[b'geo'])
print('Geo metadata:', json.dumps(geo_md, indent=2))
"
Schema:
id: string
class: string
subclass: string
name: string
geometry: binary
-- field metadata --
ARROW:extension:name: 'geoarrow.wkb'
ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 2460
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 813
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 2367
Metadata keys: [b'pandas', b'geo']
Geo metadata: {
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"crs": {
"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json",
"type": "ProjectedCRS",
"name": "WGS 84 / Pseudo-Mercator",
"base_crs": {
"name": "WGS 84",
"datum_ensemble": {
"name": "World Geodetic System 1984 ensemble",
"members": [
{
"name": "World Geodetic System 1984 (Transit)"
},
{
"name": "World Geodetic System 1984 (G730)"
},
{
"name": "World Geodetic System 1984 (G873)"
},
{
"name": "World Geodetic System 1984 (G1150)"
},
{
"name": "World Geodetic System 1984 (G1674)"
},
{
"name": "World Geodetic System 1984 (G1762)"
},
{
"name": "World Geodetic System 1984 (G2139)"
},
{
"name": "World Geodetic System 1984 (G2296)"
}
],
"ellipsoid": {
"name": "WGS 84",
"semi_major_axis": 6378137,
"inverse_flattening": 298.257223563
},
"accuracy": "2.0",
"id": {
"authority": "EPSG",
"code": 6326
}
},
"coordinate_system": {
"subtype": "ellipsoidal",
"axis": [
{
"name": "Geodetic latitude",
"abbreviation": "Lat",
"direction": "north",
"unit": "degree"
},
{
"name": "Geodetic longitude",
"abbreviation": "Lon",
"direction": "east",
"unit": "degree"
}
]
},
"id": {
"authority": "EPSG",
"code": 4326
}
},
"conversion": {
"name": "Popular Visualisation Pseudo-Mercator",
"method": {
"name": "Popular Visualisation Pseudo Mercator",
"id": {
"authority": "EPSG",
"code": 1024
}
},
"parameters": [
{
"name": "Latitude of natural origin",
"value": 0,
"unit": "degree",
"id": {
"authority": "EPSG",
"code": 8801
}
},
{
"name": "Longitude of natural origin",
"value": 0,
"unit": "degree",
"id": {
"authority": "EPSG",
"code": 8802
}
},
{
"name": "False easting",
"value": 0,
"unit": "metre",
"id": {
"authority": "EPSG",
"code": 8806
}
},
{
"name": "False northing",
"value": 0,
"unit": "metre",
"id": {
"authority": "EPSG",
"code": 8807
}
}
]
},
"coordinate_system": {
"subtype": "Cartesian",
"axis": [
{
"name": "Easting",
"abbreviation": "X",
"direction": "east",
"unit": "metre"
},
{
"name": "Northing",
"abbreviation": "Y",
"direction": "north",
"unit": "metre"
}
]
},
"scope": "Web mapping and visualisation.",
"area": "World between 85.06\u00b0S and 85.06\u00b0N.",
"bbox": {
"south_latitude": -85.06,
"west_longitude": -180,
"north_latitude": 85.06,
"east_longitude": 180
},
"id": {
"authority": "EPSG",
"code": 3857
}
},
"geometry_types": [
"LineString"
],
"bbox": [
-8239619.76474085,
4968255.274349709,
-8230043.093663243,
4981236.211015079
]
}
},
"version": "1.0.0",
"creator": {
"library": "geopandas",
"version": "1.1.3"
}
}
1:05
Now I understand the data. It's a GeoParquet file with: - CRS: EPSG:3857 (Web Mercator) - Geometry type: LineString (single, not multi) - Columns: id, class, subclass, name, geometry - 272 rows I need to: 1. Read the WKB geometries 2. Convert them from EPSG:3857 to EPSG:4326 (WGS84 lat/lon) 3. Write to a new GeoParquet file with `id` as the row group key I need to be careful about: - Keeping geometry as LineString (not MultiLineString) - Preserving all attributes - Using `id` as the key (partitioning/row-group key) Let me use pyarrow and shapely to do this. I'll read the parquet file, use pyproj to transform coordinates, and write it back. Let me first try reading with pyogrio now that I know the format:
1:05
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pyogrio
# Use explicit driver
gdf = pyogrio.read_dataframe('nyc_cycleways_webmercator.geoparquet')
print(gdf.head())
print(gdf.crs)
print(gdf.shape)
print(gdf.geometry.geom_type.unique())
print(gdf.columns.tolist())
"
Traceback (most recent call last):
File "<string>", line 4, in <module>
gdf = pyogrio.read_dataframe('nyc_cycleways_webmercator.geoparquet')
File "/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py", line 382, in read_dataframe
result = read_func(
path_or_buffer,
...<15 lines>...
**kwargs,
)
File "/usr/local/lib/python3.14/site-packages/pyogrio/raw.py", line 200, in read
return ogr_read(
get_vsi_path_or_buffer(path_or_buffer),
...<15 lines>...
datetime_as_string=datetime_as_string,
)
File "pyogrio/_io.pyx", line 1412, in pyogrio._io.ogr_read
File "pyogrio/_io.pyx", line 257, in pyogrio._io.ogr_open
pyogrio.errors.DataSourceError: 'nyc_cycleways_webmercator.geoparquet' not recognized as being in a supported file format.; It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.
1:12
The file is a GeoParquet with WKB geometry in EPSG:3857. Pyogrio doesn't support GeoParquet directly, but I can use pyarrow to read the parquet file and then use geopandas to work with the geometries. Let me use the `geoarrow.types` or use pyarrow + shapely to read WKB and then reproject. Let me read the parquet file with pyarrow, parse the WKB geometries, convert them from EPSG:3857 to EPSG:4326, and write back out as GeoParquet. I'll use `geopandas` with the `pyarrow` engine to read/write the GeoParquet file.
1:12
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
import pyarrow.parquet as pq
import shapely.wkb
import pyproj
import geopandas as gpd
import numpy as np
# Read with pyarrow
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
df = table.to_pandas()
# Parse WKB geometries
geoms = [shapely.wkb.loads(row) for row in df['geometry']]
# Check geometry types
from shapely import get_type_id
type_ids = [get_type_id(g) for g in geoms]
print('Type IDs:', set(type_ids))
# 1 = Point, 2 = LineString, 3 = Polygon, etc.
# Check sample
print('Sample geom:', geoms[0])
print('WKT:', shapely.to_wkt(geoms[0]))
"
Type IDs: {np.int32(1)}
Sample geom: LINESTRING (-8237655.109311736 4969349.090386289, -8237647.1944959415 4969335.991300315)
WKT: LINESTRING (-8237655.109312 4969349.090386, -8237647.194496 4969335.9913)
1:19
Wait, type ID 1 is Point, not LineString! But the output shows LINESTRING. Let me check - shapely GeometryType IDs: 0=Point, 1=LineString, 2=Polygon... Actually: Looking at shapely: - 0: Point - 1: LineString - 2: Polygon - 3: MultiPoint - 4: MultiLineString - 5: MultiPolygon So get_type_id returns 1 for LineString, which matches. But the metadata said geometry_types: ["LineString"]. Let me verify all are LineString.
1:19
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
import pyarrow.parquet as pq
import shapely.wkb
import shapely
import pyproj
import geopandas as gpd
from shapely import get_type_id
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
df = table.to_pandas()
geoms = [shapely.wkb.loads(row) for row in df['geometry']]
# Check all geometry types
from collections import Counter
print('Type counts:', Counter(get_type_id(g) for g in geoms))
# 1 = LineString
# Check CRS - it's EPSG:3857
# Transform to EPSG:4326
transformer = pyproj.Transformer.from_crs('EPSG:3857', 'EPSG:4326', always_xy=True)
def transform_linestring(geom):
# geom is a LineString
coords = list(geom.coords)
new_coords = [transformer.transform(x, y) for x, y in coords]
return shapely.LineString(new_coords)
new_geoms = [transform_linestring(g) for g in geoms]
# Check first few
for i, g in enumerate(new_geoms[:3]):
print(f'{i}: {shapely.to_wkt(g, trim=True)}')
print()
print('All are LineString:', all(get_type_id(g) == 1 for g in new_geoms))
# Create GeoDataFrame
new_gdf = gpd.GeoDataFrame(df.drop(columns=['geometry']), geometry=new_geoms, crs='EPSG:4326')
print(new_gdf.head())
print(new_gdf.crs)
"
Type counts: Counter({np.int32(1): 272})
0: LINESTRING (-74.000115 40.70788, -74.000044 40.707791)
1: LINESTRING (-74.006938 40.703752, -74.006921 40.703763, -74.006291 40.704161, -74.006118 40.704274, -74.004773 40.705138, -74.004599 40.70525, -74.004418 40.705367, -74.003873 40.705718, -74.003755 40.705791, -74.003691 40.705823, -74.003601 40.705854, -74.003521 40.705878, -74.003459 40.705908, -74.003394 40.705945, -74.00312 40.706116, -74.003105 40.706125, -74.00305 40.706157, -74.003016 40.706178, -74.002983 40.706197, -74.002938 40.706223, -74.002553 40.706451, -74.002311 40.706591, -74.002268 40.706615, -74.00224 40.706632, -74.002212 40.706648)
2: LINESTRING (-74.000044 40.707791, -74.000102 40.707762, -74.000136 40.707745, -74.000832 40.707382, -74.000891 40.707352, -74.001298 40.70714, -74.001326 40.707126, -74.002165 40.706675, -74.002182 40.706665, -74.002212 40.706648)
All are LineString: True
id ... geometry
0 008afca9-4118-4569-86a4-e05d2049e5ff ... LINESTRING (-74.00011 40.70788, -74.00004 40.7...
1 019954ce-599a-4f44-9509-f5df571ec8a6 ... LINESTRING (-74.00694 40.70375, -74.00692 40.7...
2 01e21e38-aa74-4a1b-aea5-0c154bc52559 ... LINESTRING (-74.00004 40.70779, -74.0001 40.70...
3 0241690c-de63-42c3-8049-dd993673823b ... LINESTRING (-74.01595 40.71166, -74.01591 40.7...
4 03118a13-69c4-4dac-8677-6d9932e88536 ... LINESTRING (-73.95846 40.75868, -73.95817 40.7...
[5 rows x 5 columns]
EPSG:4326
1:25
Now I need to write this as GeoParquet with `id` as the key. Let me check what "key" means in GeoParquet context - it's the partitioning column or the primary index. Let me write it properly using geopandas to_parquet.
1:25
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
import pyarrow.parquet as pq
import shapely.wkb
import shapely
import pyproj
import geopandas as gpd
from shapely import get_type_id
# Read source data
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
df = table.to_pandas()
# Parse WKB geometries
geoms = [shapely.wkb.loads(row) for row in df['geometry']]
# Transform from EPSG:3857 to EPSG:4326
transformer = pyproj.Transformer.from_crs('EPSG:3857', 'EPSG:4326', always_xy=True)
def transform_linestring(geom):
coords = list(geom.coords)
new_coords = [transformer.transform(x, y) for x, y in coords]
return shapely.LineString(new_coords)
new_geoms = [transform_linestring(g) for g in geoms]
# Create GeoDataFrame with WGS84 CRS
new_gdf = gpd.GeoDataFrame(df.drop(columns=['geometry']), geometry=new_geoms, crs='EPSG:4326')
# Write to GeoParquet with 'id' as the key (row group column)
new_gdf.to_parquet('/work/nyc_cycleways_wgs84.geoparquet', index=False)
print('Written successfully!')
print(f'Shape: {new_gdf.shape}')
print(f'CRS: {new_gdf.crs}')
print(f'Columns: {new_gdf.columns.tolist()}')
print(f'All LineStrings: {(new_gdf.geometry.geom_type == \"LineString\").all()}')
"
Written successfully! Shape: (272, 5) CRS: EPSG:4326 Columns: ['id', 'class', 'subclass', 'name', 'geometry'] All LineStrings: True
1:29
Now I need to verify the output. The user asked to "use `id` as the key" - that likely means using `id` as the primary key / sorting / index column in the GeoParquet metadata. Let me verify the output file.
1:29
1:29
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pyarrow.parquet as pq
import json
table = pq.read_table('nyc_cycleways_wgs84.geoparquet')
print('Schema:')
print(table.schema)
print()
# Check geo metadata
md = table.schema.metadata
geo_md = json.loads(md[b'geo'])
print('Geo metadata:', json.dumps(geo_md, indent=2))
print()
# Check pandas metadata for index info
pandas_md = json.loads(md[b'pandas'])
print('Pandas metadata:', json.dumps(pandas_md, indent=2))
"
Schema:
id: large_string
class: large_string
subclass: large_string
name: large_string
geometry: binary
-- field metadata --
ARROW:extension:name: 'geoarrow.wkb'
ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 1498
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 629
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 1384
Geo metadata: {
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"crs": {
"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json",
"type": "GeographicCRS",
"name": "WGS 84",
"datum_ensemble": {
"name": "World Geodetic System 1984 ensemble",
"members": [
{
"name": "World Geodetic System 1984 (Transit)"
},
{
"name": "World Geodetic System 1984 (G730)"
},
{
"name": "World Geodetic System 1984 (G873)"
},
{
"name": "World Geodetic System 1984 (G1150)"
},
{
"name": "World Geodetic System 1984 (G1674)"
},
{
"name": "World Geodetic System 1984 (G1762)"
},
{
"name": "World Geodetic System 1984 (G2139)"
},
{
"name": "World Geodetic System 1984 (G2296)"
}
],
"ellipsoid": {
"name": "WGS 84",
"semi_major_axis": 6378137,
"inverse_flattening": 298.257223563
},
"accuracy": "2.0",
"id": {
"authority": "EPSG",
"code": 6326
}
},
"coordinate_system": {
"subtype": "ellipsoidal",
"axis": [
{
"name": "Geodetic latitude",
"abbreviation": "Lat",
"direction": "north",
"unit": "degree"
},
{
"name": "Geodetic longitude",
"abbreviation": "Lon",
"direction": "east",
"unit": "degree"
}
]
},
"scope": "Horizontal component of 3D system.",
"area": "World.",
"bbox": {
"south_latitude": -90,
"west_longitude": -180,
"north_latitude": 90,
"east_longitude": 180
},
"id": {
"authority": "EPSG",
"code": 4326
}
},
"geometry_types": [
"LineString"
],
"bbox": [
-74.0177637,
40.7004314,
-73.931735,
40.78877799999999
]
}
},
"version": "1.0.0",
"creator": {
"library": "geopandas",
"version": "1.1.3"
}
}
Pandas metadata: {
"index_columns": [],
"column_indexes": [],
"columns": [
{
"name": "id",
"field_name": "id",
"pandas_type": "object",
"numpy_type": "str",
"metadata": null
},
{
"name": "class",
"field_name": "class",
"pandas_type": "object",
"numpy_type": "str",
"metadata": null
},
{
"name": "subclass",
"field_name": "subclass",
"pandas_type": "object",
"numpy_type": "str",
"metadata": null
},
{
"name": "name",
"field_name": "name",
"pandas_type": "object",
"numpy_type": "str",
"metadata": null
},
{
"name": "geometry",
"field_name": "geometry",
"pandas_type": "empty",
"numpy_type": "object",
"metadata": null
}
],
"attributes": {},
"creator": {
"library": "pyarrow",
"version": "24.0.0"
},
"pandas_version": "3.0.3"
}
1:35
The output looks correct. The file has been written with: 1. CRS: EPSG:4326 (WGS 84 lat/lon) - confirmed in the geo metadata 2. Geometry types: LineString - confirmed as `"geometry_types": ["LineString"]` 3. All original attributes preserved (id, class, subclass, name) 4. Bounding box shows lat/lon values (approximately NYC area) 5. The `id` column is preserved as a regular column The user asked to "use `id` as the key" - this likely means the `id` column should be the primary key/identifier. The GeoParquet file has `id` as a column. That's what was requested. The file is written to `/work/nyc_cycleways_wgs84.geoparquet` as requested.
1:35