[Python] Identify unique groupings of polygons in Geopandas / Shapely

Let's say I have two disjoint groups / "islands" of polygons (think census tracts in two non-adjacent counties). My data could look something like this:

>>> p1=Polygon([(0,0),(10,0),(10,10),(0,10)])
>>> p2=Polygon([(10,10),(20,10),(20,20),(10,20)])
>>> p3=Polygon([(10,10),(10,20),(0,10)])
>>>
>>> p4=Polygon([(40,40),(50,40),(50,30),(40,30)])
>>> p5=Polygon([(40,40),(50,40),(50,50),(40,50)])
>>> p6=Polygon([(40,40),(40,50),(30,50)])
>>>
>>> df=gpd.GeoDataFrame(geometry=[p1,p2,p3,p4,p5,p6])
>>> df
geometry
0 POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0))
1 POLYGON ((10 10, 20 10, 20 20, 10 20, 10 10))
2 POLYGON ((10 10, 10 20, 0 10, 10 10))
3 POLYGON ((40 40, 50 40, 50 30, 40 30, 40 40))
4 POLYGON ((40 40, 50 40, 50 50, 40 50, 40 40))
5 POLYGON ((40 40, 40 50, 30 50, 40 40))
>>>
>>> df.plot()




I want the polygons within each island to take on an ID (can be arbitrary) representing it's group. For example the 3 polygons on the bottom left can have IslandID = 1 and the 3 polygons on the top right can have an IslandID=2.

I have developed a way to do this, but I'm wondering if it's the best / most efficient way. I do the following:

1) Create a GeoDataFrame with a geometry equal to polygons within the multipolygon unary union. This gives me two polygons, one for each "island".

>>> SepIslands=gpd.GeoDataFrame(geometry=list(df.unary_union))
>>> SepIslands.plot()




2) Create an ID for each group.

>>> SepIslands['IslandID']=SepIslands.index+1


3) Spatial join the islands to the original polygons, so each polygon has the appropriate island id.

>>> Final=gpd.tools.sjoin(df, SepIslands, how='left').drop('index_right',1)
>>> Final
geometry IslandID
0 POLYGON ((0 0, 10 0, 10 10, 0 10, 0 0)) 1
1 POLYGON ((10 10, 20 10, 20 20, 10 20, 10 10)) 1
2 POLYGON ((10 10, 10 20, 0 10, 10 10)) 1
3 POLYGON ((40 40, 50 40, 50 30, 40 30, 40 40)) 2
4 POLYGON ((40 40, 50 40, 50 50, 40 50, 40 40)) 2
5 POLYGON ((40 40, 40 50, 30 50, 40 40)) 2


Is this indeed the best / most efficient way to do this?

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