I have a df that contains data including geocoordinates and postcodes. lat | lon | postcode 54.3077 | 12.7 | 18314 51.898 | 9.26 | 32676 I need a new colum with the NUTS2 region, so in this case the resulting df should look something like this: lat | lon | postcode | NUTS_ID 54.3077 | 12.7 | 18314 | DE80 51.898 | 9.26 | 32676 | DEA4 I found this package: https://github.com/vis4/pyshpgeocode which I managed to run. My first approach are the following two functions: def get_gc(nuts='geoc\\shapes\\nuts2\\nuts2.shp'): """ nuts -> path to nuts file """ gc = geocoder(nuts, filter=lambda r: r['LEVL_CODE'] == 2) return gc def add_nuts_to_df(df): """ df must have lon/lat geocoodinates in it This function will add a column ['NUTS_ID'] with the corresponding NUTS region """ start_time = time.time() for idx, row in df.iterrows(): df.loc[idx, 'NUTS_ID'] = get_gc().geocode(row.lat, row.lon, filter=lambda r: r['NUTS_ID'][:2] == 'DE')['NUTS_ID'] print('Done with index {}\nTime since start: {}s'.format(idx, round(time.time() - start_time, 0 ))) return df And this does work! However, it takes ~ 0.6s for one entry and some of my df have more than a million entries. Since my original dataframes usually contain postcodes I was thinking about aggregating them using a combination of groupby / apply / transform? Or is there any other (more efficient) way of doing this? I am very grateful for any help and look forward to receiving replies. Continue reading...
