geopandas

# 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:

```bash
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.

```python
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](references/crs-management.md).

## 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](references/data-structures.md).

### 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](references/geometric-operations.md).

### Joins, overlay, clip, and dissolve

- `sjo