Introduction

What tidysdmx is, who it’s for, and how it relates to pysdmx.

tidysdmx is a Python toolbox for producing SDMX-conformant data with as little ceremony as possible. It sits on top of pysdmx and adds higher-level helpers for the workflows that statistical agencies and research teams actually run every day:

Who is this for?

  • Data engineers wiring up reproducible pipelines that ingest CSVs, Excel files, or database extracts and publish SDMX-conformant outputs.
  • Statisticians and domain experts who own a mapping between their raw indicators and an official dissemination schema and want to express it without writing XML.
  • Platform teams integrating SDMX production into orchestrators such as Kedro or Airflow.

How does it relate to pysdmx?

tidysdmx wraps pysdmx — it does not reimplement it. Wherever pysdmx already provides a model class, reader, writer, or FMR client, tidysdmx calls it directly. The value tidysdmx adds is concentrated at the boundaries: turning a pandas DataFrame into a pysdmx Schema, turning an Excel workbook into a StructureMap, and turning a mapped DataFrame into the tabular shape that pysdmx writers expect.

If you already know pysdmx, you can think of tidysdmx as a set of opinionated convenience functions; if you don’t, you can use tidysdmx without ever touching pysdmx internals.

What’s covered in this guide?

  • Installation — install with pip or Poetry and verify your environment.
  • Quick start — the smallest end-to-end example.

More chapters — end-to-end workflow, SDMX concepts, FMR integration, mapping templates, validation, and LLM/agent artefacts — are in progress and will be linked here as they land.

Status

tidysdmx is under active development. Public APIs are stabilising but may still change between minor versions. Pin a version in production and check the changelog before upgrading.