Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Use when working with GeoPandas GeoDataFrame and GeoSeries workflows, vector geospatial I/O, CRS-aware spatial operations, mapping, geocoding, and GeoPandas-specific validation.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Use this skill when a task needs package-specific guidance for GeoPandas, the Python library that extends pandas with Shapely geometry columns, CRS-aware GeoSeries/GeoDataFrame objects, vector I/O, spatial analysis, mapping, geocoding, and geometry-aware testing.
GeoPandas requires Python 3.11+ for this snapshot. A base install should provide numpy, pandas, shapely, pyproj, pyogrio, and packaging.
import geopandas as gpd
from shapely.geometry import Point
gdf = gpd.GeoDataFrame({"name": ["a"], "geometry": [Point(0, 0)]}, crs="EPSG:4326")
assert gdf.crs.to_string() == "EPSG:4326"
For environment diagnostics from this skill directory:
python scripts/check_geopandas_environment.py --json
| Task signal | Read |
|---|---|
Constructing or repairing GeoDataFrame/GeoSeries, active geometry columns, CRS metadata, set_crs versus to_crs, missing/empty geometry, coordinate access, spatial-index basics | core-data-model |
Reading or writing files, GeoJSON, GPKG, WKB/WKT, Arrow, Parquet, Feather, GeoParquet/GeoArrow, PostGIS, pyogrio/Fiona, bbox/mask/columns/rows filters | io-formats |
| Spatial joins, nearest joins, overlays, clips, dissolves, predicates, buffers, distance/area, invalid geometry repair, spatial-index queries, vector analysis pipelines | spatial-operations |
Static .plot(), interactive .explore(), choropleths, folium/mapclassify/tile dependencies, geocoding, reverse geocoding, provider/network issues | mapping-geocoding |
geopandas.testing assertions, expected GeoDataFrame fixtures, equality tolerances, focused pytest selection, optional dependency test triage, repository-maintainer validation | validation-testing |
core-data-model to create/repair objects and validate CRS/geometry quality.io-formats to load inputs or persist outputs when file/database formats are involved.spatial-operations for analysis and geometry transformations.mapping-geocoding only when rendering maps or calling geocoding providers is part of the deliverable.validation-testing to assert results or maintain the repository.set_crs() only to assign known CRS metadata to existing coordinates; use to_crs() to transform coordinates.pyarrow, SQL/PostGIS, matplotlib, folium, mapclassify, geopy, and tile/basemap packages as optional workflow dependencies.The bundled references and scripts are sufficient for common GeoPandas operation without reopening the source checkout. Runtime scripts use tiny fixtures, temporary directories, or import checks and avoid network, credentials, destructive database writes, large notebooks, and benchmark-scale tasks by default.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
The English summary is being prepared. Showing the original description.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
The English summary is being prepared. Showing the original description.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
The English summary is being prepared. Showing the original description.
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
The English summary is being prepared. Showing the original description.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
The English summary is being prepared. Showing the original description.