Fast way of querying existence of a point between two dataframes

Question

I'd like to check if a point in one dataframe exists in another using Python. I have two shapefiles / GeoPandas dataframes with each over 10000 rows, and the geometry is stored as points (eg. POINT (3.14159265359 2.71828182846)). I now would like to add a column to the first dataframe, and populate it with a certain value if this point happens to be in the other dataframe.

Using .isin() works, yet very slowly compared to gpd.sjoin(foodf, bardf). I assume that .isin() brute-forces its way, whereas the latter uses indices. (Needless to say and understandably, gpd.sjoin omits rows which are not joined.)

I would like to know, how I could speed up the process. All I need is to assign 1 for points which exist in both datasets and 0 for other cases. Is there a function in GeoPandas (or another package) included helping me, or would you recommend me to use an rtree index?

import geopandas as gpd

foodf = gpd.read_file("C:\\monty.shp")
bardf = gpd.read_file("C:\\python.shp")

foodf['centroid_exists'] = foodf.centroid.isin(bardf.centroid).astype(int)
# it works, yet very slowly

Answer 1

Spatial join should work. You must have been using 'inner' join type?

‘inner’: use intersection of keys from both dfs; retain only left_df geometry column

how='left' should give you the results you want:

df = gpd.sjoin(foodf, baadf, how='left')

I just tried it on two df with ~200k Points in each and it finishes in 30 seconds: