geopandas.GeoDataFrame.to_json#

GeoDataFrame.to_json(na='null', show_bbox=False, drop_id=False, to_wgs84=False, **kwargs)[source]#

Returns a GeoJSON representation of the GeoDataFrame as a string.

Parameters:

na{‘null’, ‘drop’, ‘keep’}, default ‘null’: Indicates how to output missing (NaN) values in the GeoDataFrame. See below.
show_bboxbool, optional, default: False: Include bbox (bounds) in the geojson
drop_idbool, default: False: Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers.
to_wgs84: bool, optional, default: False: If the CRS is set on the active geometry column it is exported as WGS84 (EPSG:4326) to meet the 2016 GeoJSON specification. Set to True to force re-projection and set to False to ignore CRS. False by default.

See also

GeoDataFrame.to_file: write GeoDataFrame to file

Notes

The remaining kwargs are passed to json.dumps().

Missing (NaN) values in the GeoDataFrame can be represented as follows:

null: output the missing entries as JSON null.
drop: remove the property from the feature. This applies to each feature individually so that features may have different properties.
keep: output the missing entries as NaN.

If the GeoDataFrame has a defined CRS, its definition will be included in the output unless it is equal to WGS84 (default GeoJSON CRS) or not possible to represent in the URN OGC format, or unless to_wgs84=True is specified.

Examples

>>> from shapely.geometry import Point
>>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:3857")
>>> gdf
    col1     geometry
0  name1  POINT (1 2)
1  name2  POINT (2 1)

>>> gdf.to_json()
'{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", "properties": {"col1": "name1"}, "geometry": {"type": "Point", "coordinates": [1.0, 2.0]}}, {"id": "1", "type": "Feature", "properties": {"col1": "name2"}, "geometry": {"type": "Point", "coordinates": [2.0, 1.0]}}], "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG::3857"}}}'

Alternatively, you can write GeoJSON to file:

>>> gdf.to_file(path, driver="GeoJSON")  