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I have a polygon shapefile. Which have some numeric attributes (A, B). I clustered the shapefile in five groups (labels) based on an attribute(A) and want to dissolve them while aggregating other attributes (A, B). Code for that is:

import geopandas as gpd
# suppose clustered_df is a gdf obtained after clustering
db_clustered_dissolve = clustered_df .dissolve(by='labels'
            , aggfunc= {'labels':'count','A': 'sum', 'B': 'sum'})

The above code dissolves the dataframe by labels column and create Multi-part polygons.

I know that if I want only single-part polygons then I can use explode() on the dataframe db_clustered_dissolve but I can not accuratly calculate A, B and count for those resulted single part dissolved polygons because every single-part polygon will have same values for A, B and count as their corresponding Multi-part polygons.

I'm having this idea that I can use something that only single-part polygon would be created while dissolving and statistics for those could be calculated on the spot.

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You need to dissolve by labels and contiguity to get what you want. Therefore, you first need to identify contiguous components and then do the dissolve. That is a simple operation with libpysal.

Using the example data from geopandas:

import geopandas as gpd
import libpysal

earth = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
earth = earth.explode()  # to get single part input

# create spatial weights matrix
W = libpysal.weights.Queen.from_dataframe(earth)

# asssing component labels
earth['comp'] = W.component_labels

dissolved = earth.dissolve(['continent', 'comp'])

See libpysal documentation for more on spatial weights matrices: https://pysal.org/libpysal/

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