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sdmxlib

Note: This library has been written extensively with AI assistance (Claude Code). Users who are not comfortable with AI-generated code should take that into account before adopting it.

SDMX structural metadata library for Python. Fetch and navigate Data Structure Definitions, Codelists, Dataflows, and related artefacts from any SDMX REST endpoint — with plain attribute access, no XML or URN chasing required.

Quick start

import sdmxlib as sl

with sl.RestRegistry(sl.Provider.BIS) as reg:
    urn = sl.DataStructure.urn_for(agency="BIS", id="WS_CBPOL")
    dsd = reg.get(urn, resolve=True)

    for dim in dsd.dimensions:
        if dim.is_enumerated:
            print(f"{dim.id}: {dim.codelist.id} ({len(dim.code_ids())} codes)")
FREQ: CL_FREQ (7 codes)
REF_AREA: CL_BIS_IF_REF_AREA (44 codes)
INSTR_ASSET: CL_INSTR_ASSET (12 codes)
BORROWER_CTY: CL_BIS_IF_REF_AREA (44 codes)

Installation

uv add sdmxlib
# or: pip install sdmxlib

sdmxlib needs Python 3.14 or later.

What it does

  • Connects to any SDMX 2.1 or 3.0 REST endpoint; built-in shortcuts for ECB, BIS, ESTAT, IMF, OECD, and the SDMX Global Registry via sl.Provider
  • One read, with resolution depth as a parameter: get(urn) for a skeleton, get(urn, resolve=True) for the full reference graph, resolve=[sl.Codelist] for named types only, and partial=False for untrimmed schemes
  • Returns fully resolved domain objects with plain attribute access — no XML, no URN chasing
  • Filter collections with lambdas or the item() expression API
  • Build SDMX artefacts locally — frozen, checked as they are built — and serialise to SDMX-ML 2.1, 3.0 and 3.1 or SDMX-JSON 2.0
  • LocalRegistry — a DuckDB-backed persistent store with lazy codelists and hierarchies and direct SQL code maps; pass it as a RestRegistry's store= and everything fetched is kept for offline use
  • A path query language over both planes, on a LocalRegistry or straight against REST: reg.query(sl.q.dataflow) for structures, reg.obs(flow) for rows, reg.sql(...) as the escape hatch
  • Uniform SKOS-aligned tree API (roots/leaves/narrower/broader/ *_transitive/depth_of plus bulk projections depths/broader_map/ ancestors_map/inherit_along_broader) on Codelist, Hierarchy, CategoryScheme, MetadataStructure — and their lazy SQL variants LazyCodelist/LazyHierarchy. A codelist is keyed by code id; the other trees by path (h["CA.ON"]), since a node id may repeat across branches
  • Reference metadata via MetadataStructure (MSD), Metadataflow, and MetadataSet — attach typed footnotes, methodology notes, or quality reports to any identifiable artefact. Definitions ride in StructureMessage; populated payloads ride in MetadataMessage (<mes:GenericMetadata> in SDMX-ML 3.0; metadata-schema envelope in SDMX-JSON 2.0). Both message kinds round-trip through LocalRegistry, JSON, and XML.
  • Integrates with Polars for schema generation and data validation
  • Fully typed — IDE completion and static analysis work throughout

For AI assistants

Two condensed reference files at the repo root are written for LLM tools (Claude Code, Cursor, Copilot, etc.):

  • llms.txt — the whole library in one pass: registries and get, navigation, building artefacts, the local store, trees, observation data, reading and writing SDMX, reference metadata, and the type table
  • llms-dsl.txt — the path query language in depth: sl.q, Query / Select, projection and aggregation, edges, windows, observation queries (ObsQuery), and reg.sql()

Every Python block in both is executed by the test suite, so they track the library's actual surface. Point your assistant at them.

Upgrading

sdmxlib is pre-stability and breaks cleanly rather than carrying two spellings of the same idea. Upgrading lists every breaking change since 0.48, with the old and new spelling of each — sl.Registry is now sl.LocalRegistry, get() takes a URN and a resolution depth, the four query DSLs are one path language, and every model class is frozen.

Documentation

See the docs for the full cookbook, including:

  • Connecting to registries and choosing a resolution depth
  • Persisting artefacts in a local DuckDB store with LocalRegistry
  • Querying structures and observations with the sl.q path language
  • Fetching observation data and pivoting to wide format
  • Inspecting DSDs and building code maps for ETL pipelines
  • Generating Polars schemas from SDMX Data Structure Definitions
  • Browsing data catalogs and filtering dataflows
  • Working with constraints, hierarchies, and serialisation

Development

just check           # lint + typecheck
just test            # unit tests
just test-integration  # end-to-end tests, replayed from recorded cassettes
just ci              # the full gate: lint, typecheck, unit, docs, integration

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