This is quite simple case, but I did not find any easy way to do it so far. The idea is to get a set of distances between all the points defined in a GeoDataFrame and the ones defined in another GeoDataFrame.
import geopandas as gpd
import pandas as pd
# random coordinates
gdf_1 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0, 0], [0, 90, 120]))
gdf_2 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0], [0, -90]))
print(gdf_1)
print(gdf_2)
# distances are calculated elementwise
print(gdf_1.distance(gdf_2))
This produces the element-wise distance between points in gdf_1 and gdf_2 that share the same index (with also a warning because the two GeoSeries do not have the same index, which will be my case).
geometry
0 POINT (0.000 0.000)
1 POINT (0.000 90.000)
2 POINT (0.000 120.000)
geometry
0 POINT (0.00000 0.00000)
1 POINT (0.00000 -90.00000)
/home/seydoux/anaconda3/envs/chelyabinsk/lib/python3.8/site-packages/geopandas/base.py:39: UserWarning: The indices of the two GeoSeries are different.
warn("The indices of the two GeoSeries are different.")
0 0.0
1 180.0
2 NaN
The question is; how is it possible to get a series of all points to points distances (or at least, the unique combinations of the index of gdf_1 and gdf_2 since it is symmetric).
In this post, the solution is given for a couple of points; but I cannot find a straightforward way to combine all points in two datasets.
In this post only element-wise operations are proposed.
An analogous question was also raised on the GitHub repo of geopandas. One of the proposed solution is to use the apply method, without any detailed answer.
You have to apply over each geometry in first gdf to get distance to all geometric in second gdf.
import geopandas as gpd
import pandas as pd
# random coordinates
gdf_1 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0, 0], [0, 90, 120]))
gdf_2 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0], [0, -90]))
gdf_1.geometry.apply(lambda g: gdf_2.distance(g))
0 1
0 0.0 90.0
1 90.0 180.0
2 120.0 210.0