georgiacivicdata.columns#

georgiacivicdata.columns(name, labels=True, *, backend=None)#

Get a dataset's column dictionary.

The columns of a dataset, lookup table or geometry dataset as a tidy table: what each column means, its type, its unit and, for metrics, whether it is a numerator or denominator. This is the place to check "what does pct_proficient_learner_or_above mean, and is it 0-1?".

The dictionary lists every column a dataset can return. Which ones a given rows() call returns also depends on its detail and columns: code columns that would be empty on every row (a county row's tract_geoid, a district row's school_code) are left out.

Parameters:

  • name (str) -- A dataset, lookup table or geometry dataset name.

  • labels (bool, default True) -- True also lists the name columns that rows() joins in by default (district_name, county_name, demographic_label, ...), each right after its code, with role "label". False lists the dataset's own columns only, matching rows(labels=False). Labels come only with datasets; lookup tables and geometry datasets ignore this argument.

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

Returns:

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

  • column: the column name.

  • label: a short display name.

  • type: "integer", "number", "string", "boolean" or "date", which rows() maps to integer, float, string, boolean and date columns.

  • role: "year", a foreign key ("fk_district", "fk_school", ...), "categorical", "metric" or "label" (a joined name column) for datasets; "key" or "attribute" for lookup tables.

  • label_of: for a "label" column, the code column it names ("district_code", "demographic", ...); otherwise missing.

  • unit: "count", "proportion" (0-1 scale), a rate unit, ...

  • is_key_metric: boolean, true for the dataset's headline metric.

  • metric_component: "numerator" or "denominator" for the counts a rate is built from.

  • nullable: boolean.

  • valid_values: list column of a categorical column's values.

  • description: the full definition.

  • null_meaning: what a missing value means in this column.

Fields that don't apply (labels and units for lookup tables and joined name columns, most fields for geometry datasets) are missing. A joined name column's description and nullable come from its lookup table; see dimension().

See also

describe

the whole schema.

Examples

>>> import georgiacivicdata as gcd
>>> gcd.columns("graduation_rate_4_year_cohort")
>>> gcd.columns("graduation_rate_4_year_cohort", labels=False)
>>> gcd.columns("schools")