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SKILL.md

---
name: tidysdmx
description: >
  Use when writing Python code that uses the tidysdmx package.
---

# tidysdmx

## Installation

```bash
pip install tidysdmx
```

## API overview

### FMR & Schemas

Fetch and parse SDMX schemas from a Fusion Metadata Registry.

- `fetch_schema`: Fetch the schema of a specified artefact from an SDMX registry
- `fetch_dsd_schema`: Fetch a DSD schema from a Fusion Metadata Registry (FMR)
- `parse_artefact_id`: Parse an artefact identifier into its components: agency, id and version
- `parse_dsd_id`: Parse a DSD identifier into its components
- `create_schema_from_table`: Create a DSD, ConceptScheme, and Codelists from a DataFrame

### Structure Maps

Build, parse, validate, and write SDMX structure maps.

- `parse_mapping_template_wb`: Read an Excel mapping template and return all sheets as DataFrames
- `build_structure_map_from_template_wb`: Build a complete StructureMap object by parsing a WB-format Excel template
- `build_fixed_map`: Build a pysdmx FixedValueMap for setting a component to a fixed value
- `build_implicit_component_map`: Build a pysdmx ImplicitComponentMap for implicit mapping rules
- `build_date_pattern_map`: Build a DatePatternMap object for mapping date patterns between SDMX components
- `build_value_map`: Create a pysdmx ValueMap object mapping a source value to a target value
- `build_value_map_list`: Build a list of ValueMap objects from a pandas DataFrame, optionally including validity periods
- `build_multi_value_map_list`: Build a list of MultiValueMap objects from a pandas DataFrame
- `build_representation_map`: Build a RepresentationMap object from a pandas DataFrame using build_value_map_list
- `build_multi_representation_map`: Build a MultiRepresentationMap object from a pandas DataFrame
- `build_single_component_map`: Build a ComponentMap mapping one source component to one target component using a RepresentationMap built from a pandas DataFrame
- `collect_structure_map_artifacts`: Collect the StructureMap and all its dependent RepresentationMaps
- `validate_structure_map_references`: Validate that all RepresentationMap references are resolved
- `prepare_structure_map_for_upload`: Prepare a StructureMap for upload by collecting all dependencies

### Mapping

Apply structure maps to tidy DataFrames.

- `map_structures`: Apply all mapping components from a StructureMap to a DataFrame
- `apply_fixed_value_maps`: Apply FixedValueMap rules to a DataFrame
- `apply_implicit_component_maps`: Apply ImplicitComponentMap rules to a DataFrame
- `apply_multi_component_map`: Apply a single MultiComponentMap with regex support, preserving rule order
- `map_to_sdmx`: Map DataFrame columns to SDMX values using a lookup mapping
- `transform_source_to_target`: Transform a raw DataFrame into the format defined by a components map

### Standardisation

Prepare a mapped DataFrame for SDMX upload.

- `standardize_output`: Standardize the output DataFrame by adding SDMX reference columns
- `standardize_sdmx`: Standardize a DataFrame by applying column and value transformations
- `standardize_data_for_upload`: Standardize a DataFrame for SDMX upload
- `standardize_indicator_id`: Fix the INDICATOR column to be uppercase and prefixed with dataset ID
- `sanitize_variable`: Sanitize a raw string value into a valid SDMX code ID
- `add_sdmx_reference_cols`: Add SDMX reference columns to a DataFrame

### Validation

Validate datasets against schemas and codelists.

- `validate_dataset_local`: Validate that a DataFrame is SDMX compliant and return a DataFrame of errors
- `validate_columns`: Validate that all DataFrame columns are valid components or SDMX references
- `validate_mandatory_columns`: Validate that all mandatory columns are present in the DataFrame
- `validate_codelist_ids`: Validate that all values in coded columns are within the allowed codelist IDs
- `validate_duplicates`: Validate that there are no duplicate rows for a given set of key columns
- `validate_no_missing_values`: Validate that there are no missing values in mandatory columns

### Tidy Raw

Filter and shape raw inputs.

- `filter_tidy_raw`: Filter an SDMX DataFrame by removing rows that violate codelist constraints
- `filter_rows`: Filter out rows where values are not in the allowed codelist

### Utilities

Helpers for codelists, components, Excel templates, and XML.

- `extract_validation_info`: Extract validation information from a given schema
- `get_codelist_ids`: Retrieve all codelist IDs for given coded components
- `extract_component_ids`: Retrieve all component IDs from a given pysdmx Schema
- `create_mapping_rules`: Create Excel hyperlink formulas for components with representation maps
- `build_excel_workbook`: Build a Workbook with component mapping and representation map sheets
- `write_excel_mapping_template`: Generate an Excel mapping template with component and representation tabs
- `read_mapping`: Read a JSON mapping file and parse its content into DataFrames
- `fix_sdmx_xml_datatype_tags`: Fix incorrect SourceCodelist/TargetCodelist tags in SDMX-ML
- `gen_urn`: Generate a full SDMX URN for any maintainable artefact

### QA

Quality-assurance helpers.

- `qa_coerce_numeric`: Coerce specified columns to numeric, removing rows with invalid values
- `qa_remove_duplicates`: Remove duplicate rows from a DataFrame

### Kedro Integration

Kedro pipeline node wrappers.

- `kd_read_mappings`: Fetch multiple mappings from different files
- `kd_standardize_sdmx`: Standardize a partitioned dataset into SDMX format
- `kd_validate_dataset_local`: Validate a single DataFrame for SDMX compliance
- `kd_validate_datasets_local`: Validate multiple datasets for SDMX compliance

### Lookups

Vectorised lookup helpers.

- `vectorized_lookup_ordered_v1`: Apply ordered regex matching to a Pandas Series
- `vectorized_lookup_ordered_v2`: Apply ordered matching (regex or exact) to a Pandas Series

## Resources

- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs