# 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
```