Skip to content
cccskills
FreePublished on GitHub

geopandas

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 install

Files included (33)

  • SKILL.md4.4 KB
  • references/package-overview.md3.3 KB
  • references/repo-provenance.md2.2 KB
  • references/repo-routing-metadata.json321 B
  • references/troubleshooting.md3.5 KB
  • scripts/check_geopandas_environment.py4.6 KB
  • sub-skills/core-data-model/references/api-reference.md5.1 KB
  • sub-skills/core-data-model/references/troubleshooting.md2.6 KB
  • sub-skills/core-data-model/references/workflows.md3.5 KB
  • sub-skills/core-data-model/scripts/validate_geodataframe.py5.2 KB
  • sub-skills/core-data-model/SKILL.md3.9 KB
  • sub-skills/io-formats/references/io-reference.md4.5 KB
  • sub-skills/io-formats/references/troubleshooting.md2.8 KB
  • sub-skills/io-formats/references/workflows.md3.2 KB
  • sub-skills/io-formats/scripts/check_optional_io_dependencies.py2.8 KB
  • sub-skills/io-formats/scripts/io_roundtrip_smoke.py3.3 KB
  • sub-skills/io-formats/SKILL.md3.9 KB
  • sub-skills/mapping-geocoding/references/troubleshooting.md2.5 KB
  • sub-skills/mapping-geocoding/references/visualization-geocoding-reference.md2.6 KB
  • sub-skills/mapping-geocoding/references/workflows.md2.7 KB
  • sub-skills/mapping-geocoding/scripts/check_mapping_optional_deps.py2.5 KB
  • sub-skills/mapping-geocoding/scripts/mock_geocode_smoke.py3.3 KB
  • sub-skills/mapping-geocoding/SKILL.md3.6 KB
  • sub-skills/spatial-operations/references/operations-reference.md4.0 KB
  • sub-skills/spatial-operations/references/troubleshooting.md2.3 KB
  • sub-skills/spatial-operations/references/workflows.md2.5 KB
  • sub-skills/spatial-operations/scripts/spatial_operations_smoke.py2.9 KB
  • sub-skills/spatial-operations/SKILL.md3.5 KB
  • sub-skills/validation-testing/references/maintainer-workflows.md2.6 KB
  • sub-skills/validation-testing/references/testing-reference.md2.9 KB
  • sub-skills/validation-testing/references/troubleshooting.md2.2 KB
  • sub-skills/validation-testing/scripts/geopandas_assertion_demo.py2.3 KB
  • sub-skills/validation-testing/SKILL.md3.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

GeoPandas Repo Skill

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.

Start Here

Installation and Import Check

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

Sub-skill Routes

Task signalRead
Constructing or repairing GeoDataFrame/GeoSeries, active geometry columns, CRS metadata, set_crs versus to_crs, missing/empty geometry, coordinate access, spatial-index basicscore-data-model
Reading or writing files, GeoJSON, GPKG, WKB/WKT, Arrow, Parquet, Feather, GeoParquet/GeoArrow, PostGIS, pyogrio/Fiona, bbox/mask/columns/rows filtersio-formats
Spatial joins, nearest joins, overlays, clips, dissolves, predicates, buffers, distance/area, invalid geometry repair, spatial-index queries, vector analysis pipelinesspatial-operations
Static .plot(), interactive .explore(), choropleths, folium/mapclassify/tile dependencies, geocoding, reverse geocoding, provider/network issuesmapping-geocoding
geopandas.testing assertions, expected GeoDataFrame fixtures, equality tolerances, focused pytest selection, optional dependency test triage, repository-maintainer validationvalidation-testing

Workflow Order

  1. Use core-data-model to create/repair objects and validate CRS/geometry quality.
  2. Use io-formats to load inputs or persist outputs when file/database formats are involved.
  3. Use spatial-operations for analysis and geometry transformations.
  4. Use mapping-geocoding only when rendering maps or calling geocoding providers is part of the deliverable.
  5. Use validation-testing to assert results or maintain the repository.

Common Decisions

  • Use set_crs() only to assign known CRS metadata to existing coordinates; use to_crs() to transform coordinates.
  • Reproject to a suitable projected CRS before metric distances, areas, buffers, nearest thresholds, or distance columns.
  • Prefer base pyogrio I/O unless the task specifically needs optional Fiona behavior.
  • Treat pyarrow, SQL/PostGIS, matplotlib, folium, mapclassify, geopy, and tile/basemap packages as optional workflow dependencies.
  • Do not call real geocoding providers, database services, or remote URLs in smoke tests unless the user explicitly authorizes network/service use.
  • GeoPandas is primarily a Python API package; route command-line requests to bundled diagnostic scripts or a small public-API wrapper.

Safety and Self-containment

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

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.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

3ddfa

Free

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.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

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.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

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.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

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.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

acme

Free

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.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill