Guidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or vector-data I/O.

GeoPandas Use GeoPandas for planar vector data represented as pandas like GeoSeries and GeoDataFrame objects. This skill targets stable GeoPandas 1.2.0 (released 2026 09 28). The stable website currently carries a development build label; release specific behavior below was checked against the v1.2.0 source and wheel. Reproducible environment GeoPandas 1.2.0 requires Python 3.11+, NumPy =2, pandas =2.2, Shapely =2.1, pyproj =3.7, pyogrio =0.8, and packaging . The current pyproj wheel below requires Python 3.12+. This exact Python 3.12 snapshot was smoke tested on 2026 10 01: Keep optional plotting and PostGIS packages pinned in the project lock as well. Do not mix binary geospatial packages from incompatible package channels. Workflow 1. Inspect a vetted local layer with vector inventory.py ; retain source hashes. 2. Use crs reprojection plan.py to inspect candidate transformations. It plans only: execute an appropriate to crs() /pyproj transformation separately. 3. Audit geometry with geometry validity report.py ; compare simulated repair changes before requesting its optional new GeoPackage output. 4. Run the intended analysis with explicit predicates, CRS units, precision, and aggregation rules. spatial join audit.py measures join cardinality; it does not export joined features or perform a dissolve. 5. Use export plan.py to inspect the proposed contract, then write and reopen the actual output separately. A successful plan has not written a file. 6. Review disclosure and generalization before sharing derived geodata/maps, using sensitive coordinates checklist.py when sensitive locations occur. Examples below use synthetic or placeholder local inputs. File/database examples need the named dataset or service; PostGIS and tile provider calls were reviewed against upstream contracts but were not exercised against a live service. 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. 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 [Spatial joins ignore the third dimension](https://geopandas.org/en/stable/docs/reference/api/geopandas.sjoin.html). Features at different elevations can still match in XY. For discrete floors, strata, or survey dates, use a validated shared attribute restriction ( on attribute ) when scientifically appropriate; true 3D distance or intersection requires a method that models Z. 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](references/spatial analysis.md). 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 1.1.0 in GeoPandas 1.2. Set schema version="1.0.0" explicitly for an older consumer. Native GeoArrow encoding requires 1.1.0; bbox covering requires 1.1.0 or later. The upcoming 2.0.0 schema is opt in, WKB only, and requires PyArrow =21 for writing. 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](references/data io.md). 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 1.2 raises these floors as listed above. PostGIS hardening shipped in 1.1.2 and 1.1.4 and is included in the pinned 1.2.0. In 1.2, replace buffer(resolution=...) with quad segs=... ; remove the expired use pygeos option and use sample points(rng=...) , not seed= . Recheck plot styling/legends after the 1.2 plotting rewrite. Avoid depending on Matplotlib collection internals; static plot(tiles=...) can now fetch basemap imagery when requested. 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](references/visualization.md). 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. CLI Purpose scripts/vector inventory.py Redacted local vector/GeoParquet technical inventory scripts/crs reprojection plan.py CRS units, axes, candidate transform and antimeridian plan scripts/geometry validity report.py Dry run validity audit; optional repair to a new GeoPackage scripts/spatial join audit.py Predicate semantics, duplicate IDs and join cardinality scripts/export plan.py Non executing vector/GeoParquet export contract scripts/sensitive coordinates checklist.py Privacy/generalization release gate Reference index [Data structures](references/data structures.md) [CRS management](references/crs management.md) [Geometric operations](ref