georgiacivicdata.datasets#

georgiacivicdata.datasets(main_topic=None, level=None, year=None, kind=None, details=False, *, backend=None)#

List the datasets in the catalog.

The catalog of everything the API publishes, one row per dataset. With no arguments it returns every dataset: all subject areas, detail levels and years, tables and geometry alike. Each argument only narrows the list (the filtering runs on the server). About 176 datasets: small enough to browse or filter locally.

The six lookup tables (districts, schools, counties, tracts, block groups, demographics) are not datasets and are not listed; see dimension().

Parameters:

  • main_topic (str, optional) -- A single subject area: "education", "census", "health" or "immigration". Default: all.

  • level (str, optional) -- Keep datasets that publish rows at this detail level, such as "schools", "districts", "counties" or "tracts". Default: all.

  • year (int, optional) -- Keep datasets that cover this year. Default: all.

  • kind ({"table", "geometry"}, optional) -- Default: both.

  • details (bool, default False) -- False gives the compact catalog. True adds the long columns (description, source, grain and more).

  • backend ({"pandas", "polars"}, optional) -- The kind of data frame to return. Default: the backend option.

Returns:

pandas.DataFrame or polars.DataFrame -- One row per dataset, with columns:

  • name: the dataset's name, used by every other function.

  • title, summary: what it holds.

  • kind: "table" or "geometry".

  • main_topic: the subject area.

  • detail_levels: list column of the levels it publishes.

  • year_min, year_max: integers, the years covered.

With details=True, also: description, source, source_url, grain (list column: the columns that identify a row), default_detail, year_gaps (list column of missing years), update_frequency, search_aliases_en and search_aliases_es (list columns of everyday search phrases), and updated_at (UTC timestamp). Geometry datasets have missing values where a field doesn't apply.

See also

search

rank datasets by plain words.

describe

one dataset.

Examples

>>> import georgiacivicdata as gcd
>>> gcd.datasets()
>>> gcd.datasets(main_topic="education", level="schools")
>>> gcd.datasets(kind="geometry", details=True)