georgiacivicdata.values#

georgiacivicdata.values(name, columns, *, year=None, detail=None, district=None, school=None, county=None, backend=None, **filters)#

List the category combinations a filter covers.

Which combinations of category values exist, and how many rows each has, under the same filters as rows(). It answers "which subjects exist in 2016 at district level?" or "which demographic groups does this county have rows for?" without downloading the rows. The schema's valid_values list every value a column has ever used; values() lists what exists for this filter, which is what you need when a filter returns zero rows.

The API returns at most 500 combinations; the function warns when that cap is reached.

Parameters:

  • name (str) -- The dataset's name.

  • columns (str or list of str) -- One or more categorical columns (or "demographic"), such as "subject" or ["subject", "grade_cluster"].

  • year -- Exactly as in rows().

  • detail -- Exactly as in rows().

  • district -- Exactly as in rows().

  • school -- Exactly as in rows().

  • county -- Exactly as in rows().

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

  • **filters -- Named filters, exactly as in rows().

Returns:

pandas.DataFrame or polars.DataFrame -- Most rows first, one column per requested column (typed from the schema; missing where the column is empty for those rows) and n_rows (integer), the number of rows with that combination.

See also

rows

the rows themselves.

describe

every filter and its allowed values.

Examples

>>> import georgiacivicdata as gcd
>>> gcd.values(
...     "ccrpi_content_mastery", "subject", year=2016, detail="districts"
... )
>>> gcd.values(
...     "graduation_rate_4_year_cohort", "demographic",
...     district="Fulton County", year=2024,
... )