geopandas.GeoSeries.distance

GeoSeries.distance(other, align=True)

Returns a Series containing the distance to aligned other.

The operation works on a 1-to-1 row-wise manner:

Parameters

otherGeoseries or geometric object

The Geoseries (elementwise) or geometric object to find the distance to.

alignbool (default True)

If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.

Returns

Series (float)

Examples

>>> from shapely.geometry import Polygon, LineString, Point
>>> s = geopandas.GeoSeries(
...     [
...         Polygon([(0, 0), (1, 0), (1, 1)]),
...         Polygon([(0, 0), (-1, 0), (-1, 1)]),
...         LineString([(1, 1), (0, 0)]),
...         Point(0, 0),
...     ],
... )
>>> s2 = geopandas.GeoSeries(
...     [
...         Polygon([(0.5, 0.5), (1.5, 0.5), (1.5, 1.5), (0.5, 1.5)]),
...         Point(3, 1),
...         LineString([(1, 0), (2, 0)]),
...         Point(0, 1),
...     ],
...     index=range(1, 5),
... )

>>> s
0    POLYGON ((0.00000 0.00000, 1.00000 0.00000, 1....
1    POLYGON ((0.00000 0.00000, -1.00000 0.00000, -...
2        LINESTRING (1.00000 1.00000, 0.00000 0.00000)
3                              POINT (0.00000 0.00000)
dtype: geometry

>>> s2
1    POLYGON ((0.50000 0.50000, 1.50000 0.50000, 1....
2                              POINT (3.00000 1.00000)
3        LINESTRING (1.00000 0.00000, 2.00000 0.00000)
4                              POINT (0.00000 1.00000)
dtype: geometry

We can check the distance of each geometry of GeoSeries to a single geometry:

>>> point = Point(-1, 0)
>>> s.distance(point)
0    1.0
1    0.0
2    1.0
3    1.0
dtype: float64

We can also check two GeoSeries against each other, row by row. The GeoSeries above have different indices. We can either align both GeoSeries based on index values and use elements with the same index using align=True or ignore index and use elements based on their matching order using align=False:

>>> s.distance(s2, align=True)
0         NaN
1    0.707107
2    2.000000
3    1.000000
4         NaN
dtype: float64

>>> s.distance(s2, align=False)
0    0.000000
1    3.162278
2    0.707107
3    1.000000
dtype: float64

geopandas.GeoSeries.geom_type geopandas.GeoSeries.representative_point