For loop for minimum distance between points in dataframe and polygon in another dataframe

Question

I want to calculate the distance from each point of dataframe geosearch_crs to the polygons in the gelb_crs dataframe, returning only the minimum distance.
I have tried this code:

for i in range(len(geosearch_crs)):
point = geosearch_crs['geometry'].iloc[i]
for j in range(len(gelb_crs)):
    poly = gelb_crs['geometry'].iloc[j]
    print(point.distance(poly).min())

it returns this error:

AttributeError: 'float' object has no attribute 'min'

I somehow don't get how to return what i want, the points.distance(poly).min() function should work though.

This is part of the data frames (around 180000 entries): geosearch_crs:

count	geometry
12	POINT (6.92334 50.91695)
524	POINT (6.91970 50.93167)
5	POINT (6.96946 50.91469)

gelb_crs (35 entries):

name	geometry
Polygon 1	POLYGON Z ((6.95712 50.92851 0.00000, 6.95772...
Polygon 2	POLYGON Z ((6.91896 50.92094 0.00000, 6.92211...

Answer 1

I'm not sure about the 'distance' method, but maybe you could try adding the distances to a list:

distances = list()

for i in geosearch_crs:
    for j in gelb_crs:
        distances.append(i.distance(j))

print(min(distances))

Answer 2

• your sample polygon data is unusable as it's truncated with ellipses. Have used two other polygons to deomonstrate as a MWE
• need to ensure that CRS in both data frames is compatible. Your sample data is clearly in two different CRS, points look like epsg:4326, polygons are either a UTM CRS or EPSG:3857 from the range of values
• geopandas sjoin_nearest() is simple way to find nearest polygon and get distance. Have use UTM CRS so that distance is in meters rather than degrees

import geopandas as gpd
import pandas as pd
import shapely
import io

df = pd.read_csv(
    io.StringIO(
        """count,geometry
12,POINT (6.92334 50.91695)
524,POINT (6.91970 50.93167)
5,POINT (6.96946 50.91469)"""
    )
)

geosearch_crs = gpd.GeoDataFrame(
    df, geometry=df["geometry"].apply(shapely.wkt.loads), crs="epsg:4326"
)

# generated as sample in question unusable
df = pd.read_csv(
    io.StringIO(
        '''name,geometry
Polygon 1,"POLYGON ((6.9176561 50.8949742, 6.9171649 50.8951417, 6.9156967 50.8957149, 6.9111788 50.897751, 6.9100077 50.8989409, 6.9101989 50.8991319, 6.9120049 50.9009167, 6.9190374 50.9078591, 6.9258157 50.9143227, 6.9258714 50.9143691, 6.9259546 50.9144355, 6.9273598 50.915413, 6.9325715 50.9136438, 6.9331018 50.9134553, 6.9331452 50.9134397, 6.9255391 50.9018725, 6.922309 50.8988869, 6.9176561 50.8949742))"
Polygon 2,"POLYGON ((6.9044955 50.9340428, 6.8894236 50.9344297, 6.8829359 50.9375553, 6.8862995 50.9409307, 6.889446 50.9423764, 6.9038401 50.9436598, 6.909518 50.9383374, 6.908634 50.9369064, 6.9046363 50.9340648, 6.9045721 50.9340431, 6.9044955 50.9340428))"'''
    )
)

gelb_crs = gpd.GeoDataFrame(
    df, geometry=df["geometry"].apply(shapely.wkt.loads), crs="epsg:4326"
)

geosearch_crs.to_crs(geosearch_crs.estimate_utm_crs()).sjoin_nearest(
    gelb_crs.to_crs(geosearch_crs.estimate_utm_crs()), distance_col="distance"
)

	count	geometry	index_right	name	distance
0	12	POINT (354028.1446652143 5642643.287732874)	0	Polygon 1	324.158
2	5	POINT (357262.7994182631 5642301.777981625)	0	Polygon 1	2557.33
1	524	POINT (353818.4585403281 5644287.172541857)	1	Polygon 2	971.712