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GeoPandas

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Manage and analyze spatial data with GeoPandas tools.

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Free · Opens the source repo

What GeoPandas does

GeoPandas is a powerful library designed for handling planar vector data using pandas-like GeoSeries and GeoDataFrame objects. This skill is tailored for users working with GeoPandas version 1.1.4, providing guidance and local audit tools specifically for Python workflows that involve spatial operations and vector-data input/output. It is particularly useful for data scientists, geospatial analysts, and developers who require robust tools for spatial data manipulation and analysis.

The skill includes a set of scripts and references that facilitate various operations such as geometry validity checks, spatial joins, and coordinate reference system (CRS) management. Users can expect to find tools for auditing data integrity, ensuring that geometries are valid, and managing CRS effectively. The skill emphasizes the importance of correctness in spatial operations, providing gates to validate results before trusting them, which is crucial for maintaining data quality in geospatial projects.

GeoPandas also supports various data I/O operations, allowing users to read from and write to different geospatial formats. The bundled scripts help automate common tasks, such as generating geometry validity reports and checking for sensitive coordinates. This makes the skill a valuable addition for anyone looking to streamline their geospatial data workflows while ensuring compliance with safety and privacy standards.

Overall, GeoPandas is an essential tool for developers and data scientists who work with geospatial data, providing a comprehensive suite of functionalities to manage, analyze, and visualize spatial information effectively.

When to use it

Use this skill when working with GeoPandas for spatial data analysis, especially in Python environments.

When not to use it

This skill may not be suitable for users not working with GeoPandas or those needing advanced geospatial features beyond the scope of GeoPandas 1.1.4.

What you can build with it

Spatial Data Analysis

Analyze and visualize spatial datasets using GeoPandas to derive insights and patterns.

Geometry Validity Checks

Use the geometry validity reports to ensure that your geospatial data meets quality standards before analysis.

Coordinate Reference System Management

Manage and transform CRS for your datasets effectively, ensuring accurate spatial measurements.

How to install GeoPandas

View source

1. Install with the skills CLI

npx skills add k-dense-ai/scientific-agent-skills/geopandas --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by k-dense-ai

GeoPandas

Use GeoPandas for planar vector data represented as pandas-like GeoSeries and GeoDataFrame objects. This skill targets stable GeoPandas 1.1.4 (released 2026-06-26), not the unreleased 1.2 documentation.

Reproducible environment

GeoPandas 1.1.4 requires Python 3.10+; its tagged source requires NumPy >=1.24, pandas >=2.0, Shapely >=2.0, pyproj >=3.5, pyogrio >=0.7.2, and packaging. This exact Python 3.12 snapshot was smoke-tested on 2026-07-23:

uv venv --python 3.12
uv pip install \
  "geopandas==1.1.4" \
  "numpy==2.5.1" \
  "pandas==3.0.5" \
  "shapely==2.1.2" \
  "pyproj==3.7.2" \
  "pyogrio==0.13.0" \
  "pyarrow==25.0.0" \
  "packaging==26.2"

Keep optional plotting and PostGIS packages pinned in the project lock as well. Do not mix binary geospatial packages from incompatible package channels.

Safety and privacy contract

  • Treat exact coordinates, addresses, parcel boundaries, trajectories, and small-area joins as sensitive. Default reports to counts, categories, coarse extents, and redacted identifiers. Generalize before publication.
  • Never automatically load a URL, cloud URI, GDAL /vsi* path, archive, or geocode an address. Obtain explicit approval, validate provenance and hashes, then stage an unpacked local file in an isolated workspace.
  • GDAL/OGR drivers, GEOS, PROJ, pyogrio, Shapely, pyproj, and their wheels are a native-code trust boundary. Prefer official wheels/conda-forge, record native versions, restrict drivers, and process untrusted data in a sandbox.
  • Do not open macro-enabled office files or nested archives through permissive GDAL drivers. The bundled CLIs use an extension allowlist and reject archives.
  • Read only named database secrets such as GEOPANDAS_POSTGIS_PASSWORD; use a secret manager or scoped environment variable. Never embed a password in a URL or source, print an engine/URL, or dump the environment.
  • Every derived artifact needs source hashes/versions, CRS, operation parameters, predicate, join cardinality, precision/repair choices, and row-count checks.

Correctness gates

Apply these gates before trusting a result:

  1. Identity and provenance — identify the source layer, stable feature key, duplicate IDs, row count, geometry column, parser/driver, and content hash.
  2. Geometry state — count null, empty, invalid, mixed, Z/M, and collapsed geometries separately. None is missing; an empty Shapely geometry is real.
  3. CRS semantics — require CRS metadata. set_crs() assigns metadata; to_crs() transforms coordinates. Never guess a CRS from coordinate ranges.
  4. Units and operation — GeoPandas is planar. Geographic coordinates are angular; do not use them directly for buffer, distance, area, nearest joins, precision grids, or tolerances. Choose a fit-for-purpose local/equal-area CRS or a geodesic method.
  5. Transform quality — inspect axis order, area of use, datum pipeline, expected accuracy, ballpark status, and missing grids. Keep PROJ network disabled unless the user explicitly approves grid retrieval.
  6. Topology and precision — validate before and after repair/overlay. Pick a precision grid from source accuracy and CRS units; arbitrary snapping can collapse features or create bias.
  7. Cardinality — state expected one-to-one, one-to-many, or many-to-many behavior before merge, sjoin, or sjoin_nearest; audit unmatched and multiplied rows afterward.
  8. Output contract — use a new output path, preserve a stable feature ID, document schema/CRS/encoding, reopen the artifact, and compare counts/types.

CRS and antimeridian rules

GeoPandas stores CRS as pyproj.CRS. Coordinate arrays use traditional GIS (x, y) order, while authority definitions can advertise latitude-first axes. Use Transformer(..., always_xy=True) for explicit coordinate-array pipelines, and record that choice.

to_crs() transforms vertices and assumes each segment is straight in the source CRS; it does not transform geodesic arcs. Geometries crossing ±180° or a projection boundary can be badly wrapped. Detect crossings, split/unwrap and densify in a documented geographic representation, transform parts, then validate. Do not use Web Mercator as a general measurement CRS.

crs = gdf.crs  # a pyproj.CRS when present
if crs is None or crs.is_geographic:
    raise ValueError("Choose a justified projected CRS before planar measurement")

unit_names = [axis.unit_name for axis in crs.axis_info]
areas = gdf.geometry.area  # square CRS units, not automatically square metres

See CRS management.

Core API decisions

Data structures

  • A GeoDataFrame can hold multiple geometry columns, each with CRS metadata, but only active_geometry_name drives frame-level spatial operations.
  • Binary GeoSeries methods are row-wise and align by index by default. Use align=False only when positional pairing is explicitly intended and lengths and order were verified.
  • Duplicate column names and duplicate feature IDs are ambiguous; reject or resolve them before joins and exports.

See data structures.

Geometry validity, precision, and union

Use is_valid and redacted is_valid_reason() categories before make_valid(method="linework"|"structure", keep_collapsed=...). Repair can change geometry type or dimension; retain the original and compare counts, area, types, empties, and collapsed parts.

set_precision(grid_size, mode=...) uses CRS units and may remove duplicate vertices or collapse features. union_all(method="unary", grid_size=...) is the robust default. Use coverage only after is_valid_coverage() proves non-overlap and edge matching; use disjoint_subset with Shapely >=2.1 when its partitioning assumption is useful.

See geometric operations.

Joins, overlay, clip, and dissolve

  • sjoin predicates are directional: left.within(right) is not left.contains(right). intersects includes boundary contact; contains excludes boundary-only points, while covers includes boundary points.
  • predicate="dwithin" requires distance; scalar or per-left-row distances are in CRS units. sjoin_nearest returns all equidistant nearest matches and does not implement a k= parameter.
  • overlay(..., make_valid=True) repairs invalid input but can change types; keep_geom_type=None drops other types with a warning. Precision mismatch can create slivers; quantify them rather than silently deleting them.
  • clip dissolves the mask. Rectangle clipping is fast but possibly dirty and may omit a line collapsed to a point; validate its output.
  • dissolve combines groupby.agg with union_all; choose explicit attribute aggregations and audit null group keys.

See spatial analysis.

I/O, Arrow, and PostGIS

GeoPandas 1.x defaults to pyogrio. Driver availability and semantics come from the installed GDAL, not GeoPandas alone. Prefer local GeoPackage for general interchange and WKB GeoParquet for columnar interoperability.

GeoParquet defaults to stable schema 1.0.0. Native GeoArrow encodings and bbox covering require schema 1.1.0 and remain less interoperable. A missing GeoParquet crs key means OGC:CRS84; explicit crs: null means unknown—do not conflate them. Reopen and validate every export.

Use parameterized SQL and a SQLAlchemy Engine/Connection for PostGIS. if_exists="replace" is destructive; default to "fail" and use a transaction.

See data I/O.

Migration checklist

For code moving from GeoPandas 0.14 or earlier:

  • GeoPandas 1.0 supports Shapely >=2 only; PyGEOS, Shapely <2, and the rtree spatial-index backend were removed.
  • pyogrio replaced Fiona as the installed/default I/O engine. Set engine= explicitly and test schema, empty, datetime, encoding, and append behavior.
  • Replace sjoin(op=...) with predicate=, sindex.query_bulk() with sindex.query(), unary_union with union_all(), and GeometryArray.data with to_numpy()/np.asarray.
  • Replace read_file(include_fields=...|ignore_fields=...) with columns=. Use schema_version=, not the removed GeoParquet version= compatibility.
  • Do not use removed geopandas.datasets, internal geopandas.io.* entry points, plot axes/colormap, or set-operation operators.
  • explode() now defaults index_parts=False; a named Series passed to set_geometry() supplies the new active-column name; a named right index can replace index_right in sjoin output.
  • Do not assign .crs to override metadata or rely on deprecated set_geometry(drop=...); use explicit set_crs() and rename/drop steps.
  • GeoPandas 1.1 requires Python >=3.10, pandas >=2.0, NumPy >=1.24, and pyproj

    =3.5. Version 1.1.2 fixed SQL injection through a PostGIS geometry-column name; the pinned 1.1.4 includes that fix.

Plotting and exploration

Maps are analytical outputs: label units, classification method, missing data, normalization denominator, and date. explore() can expose every attribute in tooltips/popups and contact tile/CDN servers; generalize first and use tiles=None, tooltip=False, and popup=False for a local draft.

See visualization.

Bundled local CLIs

All helpers are deterministic, reject network/archive paths, bound input bytes and feature counts, keep imports lazy so --help is dependency-free, and emit JSON without coordinates or record identifiers.

CLIPurpose
scripts/vector_inventory.pyRedacted local vector/GeoParquet technical inventory
scripts/crs_reprojection_plan.pyCRS units, axes, candidate transform and antimeridian plan
scripts/geometry_validity_report.pyDry-run validity audit; optional repair to a new GeoPackage
scripts/spatial_join_audit.pyPredicate semantics, duplicate IDs and join cardinality
scripts/export_plan.pyNon-executing vector/GeoParquet export contract
scripts/sensitive_coordinates_checklist.pyPrivacy/generalization release gate
python skills/geopandas/scripts/vector_inventory.py --help
python skills/geopandas/scripts/crs_reprojection_plan.py \
  --source-crs EPSG:4326 --target-crs EPSG:32631
python skills/geopandas/scripts/geometry_validity_report.py data.gpkg
python skills/geopandas/scripts/spatial_join_audit.py points.gpkg zones.gpkg \
  --predicate within --left-id point_id --right-id zone_id
python skills/geopandas/scripts/export_plan.py data.gpkg result.parquet \
  --format geoparquet --schema-version 1.0.0 \
  --stable-id-column feature_id --id-unique-verified
python skills/geopandas/scripts/sensitive_coordinates_checklist.py \
  --public-output --precise-points --contains-addresses

Reference index

Sources (verified 2026-07-23)

Frequently asked questions about GeoPandas

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