pycancensus.find_census_vectors

pycancensus.find_census_vectors(query: str, dataset: str, type: str = 'all', query_type: str = 'exact', interactive: bool = False, use_cache: bool = True, quiet: bool = False, api_key: str | None = None) → DataFrame[source]

Find census vectors using exact, semantic, or keyword search.

Mirrors R cancensus’s find_census_vectors(). Exact search matches the query literally against vector details. Semantic search tolerates spelling and phrasing differences via n-gram edit-distance matching. Keyword search splits the query into words and ranks vectors by how many of them match.

Parameters:

  • query (str) – Search query.

  • dataset (str) – The dataset to search in (e.g., ‘CA16’).

  • type (str, default "all") – Filter by vector type: ‘all’, ‘total’, ‘male’, or ‘female’.

  • query_type (str, default "exact") – One of ‘exact’, ‘semantic’, or ‘keyword’.

  • interactive (bool, default False) – For keyword search: prompt to show lower-precision matches beyond the top-ranked results.

  • use_cache (bool, default True) – If True, uses cached vector list if available.

  • quiet (bool, default False) – When True, suppress messages and warnings.

  • api_key (str, optional) – API key for CensusMapper API.

Returns:

Matching vectors with columns vector, type, label, details.

Return type:

pd.DataFrame

Examples

>>> import pycancensus as pc
>>> pc.find_census_vectors('Oji-cree', dataset='CA16', type='total')
>>> pc.find_census_vectors('after tax income', 'CA16', query_type='semantic')
>>> pc.find_census_vectors('commute duration', 'CA16', query_type='keyword')