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If df1 is the dataframe with geometry, you can do something like this: df = df1.merge(df2, on='geoid', how='left') If you are using CARTO, another solution could be using JOINs as explained here.


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You should specify a common key using on parameter. I removed merged_ prefix for legibility. df = spatial_df.merge(tab_df, on='mukey', how='left') # df = tab_df.merge(spatial_df, on='mukey', how='right') gdf = gpd.GeoDataFrame(df) Sample spatial_df: col1 col2 mukey geometry 0 A 1.76 1 ... 1 B 0.40 2 ... 2 C 0.97 3 ... ...


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You need to be more careful about your code. Already when you write layer = fields you change your dataframe to become a simple list. Your function denom doesn't return anything, and it should return a dataframe given how you use it in the multiprocessing. You never use Parallel in your code (it does not exist indeed), but use Pool. That's what you ...


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