georgiacivicdata.school_zone_coverage#

georgiacivicdata.school_zone_coverage(district_code=None, district=None, *, geometry=False, backend=None)#

Get each district's attendance-zone coverage.

How completely each standard district's attendance zones were collected, one row per district and zone level: the source tier, the status of each level, the share of the district's land and 2020 population the zones cover, how they were built, and the reason for anything short of complete.

The API returns one row per district with a column per level (level_status_elementary, pct_population_middle, ...); this function pivots those columns long. The district filter runs locally (the route takes no filters; the table has about 180 rows).

Parameters:

  • district_code -- As in district_boundaries().

  • district -- As in district_boundaries().

  • geometry (bool, default False) -- False returns a data frame; True returns a GeoDataFrame of the district outlines carrying the same fields.

  • backend ({"pandas", "polars"}, optional) -- With geometry=False, the kind of data frame to return.

Returns:

DataFrame or geopandas.GeoDataFrame -- One row per district and zone level, with columns year (integer), district_code, district_name, tier (the source tier, A to E or N), status (the district's overall status), zone_level, level_status ("zoned", "one_school_per_grade", "partial", "not_available", "served_by_other_district"), pct_land_area and pct_population (0-1, the share the level's zones cover), zone_count, methods, accuracy_classes, reason, then levels_covered, currency_statuses, best_lead, extension_note and served_elsewhere (a list column of dicts: the other district's school serving that level, with district_code, school_code and reason).

See also

school_zones

the zones.

Examples

>>> import georgiacivicdata as gcd
>>> gcd.school_zone_coverage(district="Clay County")
>>> coverage = gcd.school_zone_coverage()
>>> coverage.groupby(["zone_level", "level_status"]).size()