# Quick start

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

``` python
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

``` python
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](../reference/build_structure_map_from_template_wb.md#tidysdmx.build_structure_map_from_template_wb)):

``` python
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](../reference/index.md) documents every public function.
- A full end-to-end workflow walkthrough and recipe collection are in progress.