I have these columns:

index, area_of_poly, cad_admin

I have to group by index (it is a normal column called index) in order to take the rows that have the same value.

#all the ones, all the twos, etc

Some of them (rows) are unique though. About the ones that are not unique now:

What I have done so far: I have to check with a group by which of the groups have the largest area and give its respected cad_admin value to the others in its group in a new column called cad_admin1. The unique values are going to still have the same value they had in cad_admin in the now cad_admin1 column.

more info:

the dataset: http://www.mediafire.com/file/x4q5k7xuztq6o3w/p.zip

import geopandas as gpd


359    9
391    8
376    7
374    6
354    5
446    4
403    4
348    4
422    4
424    4
451    4
364    3
315    3
100    3
245    3

inte["rank_gr"] = inte.groupby("index")["area_of_poly"].rank(ascending = False, method = 
inte["key1_temp"] = inte.apply(lambda row: str(row[""]) if row["rank_gr"] == 1.0
else "", axis = 1)
inte["CAD_ADMIN_FINAL"] = inte.groupby("index")["key1_temp"].transform("sum")
print (inte[["area_of_poly", "index", "CAD_ADMIN", "CAD_ADMIN_FINAL"]])

This code as you will see produces some errors like:

TypeError: 'str' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

KeyError: ('', 'occurred at index 0')

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