I am wondering whether it is possible to identify all neighbors to each polygon using only python (with, e.g., geopandas) in the same way that can be done with python in QGIS (Find neighbors polygon).


The following script finds and adds neighbors as a new field value joined by comma.

import geopandas as gpd

file= "C:/path/to/shapefile.shp"    

# open file
gdf = gpd.read_file(file)

# add NEIGHBORS column
gdf["NEIGHBORS"] = None  

for index, country in gdf.iterrows():   

    # get 'not disjoint' countries
    neighbors = gdf[~gdf.geometry.disjoint(country.geometry)].NAME.tolist()

    # remove own name of the country from the list
    neighbors = [ name for name in neighbors if country.NAME != name ]

    # add names of neighbors as NEIGHBORS value
    gdf.at[index, "NEIGHBORS"] = ", ".join(neighbors)
# save GeoDataFrame as a new file

enter image description here


This is an addendum to Kadir's answer (which works great).

For one, instead of using not disjoint you can just use touches directly, which does the same thing but is easier to read.

If, as the OP asked, you want to search internally to find all neighboring geometries in a single geoDataframe:

for index, row in df.iterrows():  
    neighbors = df[df.geometry.touches(row['geometry'])].name.tolist() 
    neighbors = neighbors.remove(row.name)
    df.at[index, "my_neighbors"] = ", ".join(neighbors)

For a related capability you may want to determine whether each row of a dataframe borders some particular other shape, potentially from a different dataframe or some input value (converted into a geoDataframe).

someSpecialGeometry = GEOdataframeRow['geometry'].values[0] 
df['isNeighbor'] = df.apply(lambda row: row['geometry'].touches(someSpecialGeometry), axis=1)

This creates a boolean-valued column for whether each row borders the shape of interest.


This is a further addendum relating to Aaron's comment.

If you find 'touches' is failing to find all neighboring polygons like it should this is likely down to the resolution of your polygon borders which may in fact intersect hence 'touches' ignoring them.

I solved this issue using 'overlaps' additionally to 'touches'.

neighbors = np.array(df[df.geometry.touches(row['geometry'])].name)
#overlapping neighbors use if discrepances found with touches
overlap = np.array(df[df.geometry.overlaps(row['geometry'])].name)

neighbors = np.union1d(neighbors, overlap)

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