Search for statistical datasets by keyword.

Search works best with Czech terms: obyvatelstvo=population, mzdy=wages, ceny=prices, nezaměstnanost=unemployment, průmysl=industry, HDP=GDP, inflace=inflation, vzdělání=education, zdraví=health.

Returns dataset codes usable with get_dataset() and get_dataset_selections().

Search for predefined data tables (selections) by keyword.

Selections are pre-configured views of datasets — the easiest way to get data. Retrieve their data with get_selection_data(code). Search in Czech for best results.

List all available datasets with pagination.

Use offset and limit to page through results. Total: ~730 datasets.

List all predefined data tables (selections) with pagination.

Selections are pre-configured data views that can be fetched directly. Use get_selection_data(code) to retrieve their data.

Get detailed info about a dataset: description, dimensions, indicators, keywords.

Use this to understand dataset structure before querying data. The dimension codes and indicator codes are needed for get_value() and custom_query().

List predefined data tables for a specific dataset.

These selections can be fetched directly with get_selection_data(code). This is the recommended way to find available pre-built data views.

Get possible values for a dimension (e.g., years, regions, categories).

Args: dimension_code: Dimension code from get_dataset() output (e.g., CasR, Uz0). level: Filter by hierarchy level code (e.g., STAT, KRAJ, OKRES). offset: Skip first N items. limit: Max items to return (default 50, max 200).

Item codes are needed for get_value() and custom_query().

Get detailed information about a statistical indicator.

Returns the indicator's full definition, display format, and related datasets.

Fetch actual statistical data from a predefined selection as CSV.

This is the primary and most reliable way to get data. Find selection codes via search_selections() or get_dataset_selections().

Args: selection_code: Selection code (e.g., CEN0101HT01). max_rows: Maximum number of data rows to return (default 100). Set to 0 for unlimited (use with caution — some tables are very large).

Get a single specific value from a dataset.

This is the most precise way to query data — returns exactly one value. Requires knowing the exact dimension and item codes from get_dataset() and get_dimension_items().

Args: dataset_code: Dataset code (e.g., RSO01). indicator_code: Indicator code (e.g., 3971b). dimension_codes: List of dimension codes (e.g., ["CasR", "TYPPROSJED", "UZ023H2U"]). item_codes: List of item codes matching dimension_codes order (e.g., ["2023", "501", "CZ"]). version: Dataset version (optional, defaults to latest).

Example: Number of municipalities in Czech Republic in 2023: get_value("RSO01", "3971b", ["CasR","TYPPROSJED","UZ023H2U"], ["2023","501","CZ"])

Execute a custom data query on a dataset (advanced).

IMPORTANT: Prefer get_selection_data() for predefined tables — it is much simpler and more reliable. Use custom_query only when no suitable predefined selection exists.

Caveats:

• All dataset dimensions must be placed in columns, rows, or table_filters.

• Datasets with multiple time dimensions (e.g., CasM + CasR + CASRMX) may only work with certain dimension combinations matching predefined selections.

• Hierarchical territory dimensions may need "filtr" with "urovenHierarchieKod".

Args: dataset_code: Dataset code. dataset_version: Dataset version string from get_dataset(). columns: Column dimensions. Each dict must have "kodDimenze" (str). Optionally add "filtr" with [{"zobrazitPolozky": ["code1","code2"]}]. rows: Row dimensions. Same structure as columns. Use "kodDimenze": "#UKAZATEL" to put indicators as rows. table_filters: Header/filter dimensions. Same structure, but can also include "filtrTabulkyKod" (str) to filter to a single item. max_rows: Max CSV rows to return.

Get metadata about dataset content: record count, time range, last update.

Args: dataset_code: Dataset code. version: Dataset version from get_dataset().