Quick start

The smallest end-to-end tidysdmx example.

This page shows the smallest possible tidysdmx pipeline: tidy DataFrame in, validated SDMX-ready output out. A full iterative-development walkthrough (with FMR fetch, Excel mapping, and XML export) is in progress.

1. Describe your tidy raw data as a schema

import pandas as pd
from tidysdmx import create_schema_from_table

tidy_raw = pd.DataFrame({
    "SERIES":      ["PER_ALLSP_ADQ_EP_TOT"] * 4,
    "ECONOMY":     ["GHA", "GHA", "KEN", "KEN"],
    "TIME_PERIOD": ["2018", "2019", "2018", "2019"],
    "VALUE":       [12.3, 13.1, 9.8, 10.4],
})

raw_schema = create_schema_from_table(
    tidy_raw,
    dimensions=["SERIES", "ECONOMY"],
    time_dimension="TIME_PERIOD",
    measure="VALUE",
)

raw_schema is a pysdmx Schema object containing a DSD, a ConceptScheme, and the codelists inferred from your data.

2. Validate against that schema

from tidysdmx import validate_dataset_local

errors = validate_dataset_local(
    df=tidy_raw,
    schema=raw_schema.dsd.to_schema(),
    sdmx_cols=[],
)
assert errors.empty

3. Apply a structure map (when you have one)

With a StructureMap from pysdmx (or one built via build_structure_map_from_template_wb):

from tidysdmx import map_structures, standardize_output

mapped = map_structures(df=tidy_raw, structure_map=sm, verbose=True)

out = standardize_output(
    df=mapped,
    artefact_id="WB.GGH.HSP:DS_ASPIRE(1.0.0)",
    schema=dis_schema,
    action="I",
)

out is a tidy DataFrame whose columns and codes match the dissemination schema, ready to be written as SDMX-ML or uploaded to FMR.

Where to next?

• The API reference documents every public function.
• A full end-to-end workflow walkthrough and recipe collection are in progress.