Retrieving values based on other values (dataframe) - how to make my code more efficient?

Question

So after much trying I've managed to get something a bit closer to what I intend to do.

Scenario is as follows, a dataframe with many columns of which one contains unique values. Lets say this column is called "Customer Name". This maps with a one to many match on a different column lets call that one "Customer Alias".

I need a dictionary that for each customer name as a key has as value a list containing every possible customer alias for that customer name.

mapping_customers = {x:list() for x in data['Customer Name']}
for each in mapping_customers.keys():
    try:
         selection_rows = data.loc[data['Customer Name'] == each]
         mapping_customers[each].append(selection_rows['Customer Alias'])
    
    except Exception as err:
        
        print(err.args)

Right now the keys portion is working correctly but when appending the Customer Aliases they are not appended as individual items(strings) on a list. I basically get the Series in a list as the values in index position 0 and in index position 1, I see

'Series([], Name: Customer Alias, dtype: object)'

I would like to have each customer alias for a given customer name appear as an individual entry of that list.

Here is an example of what things would look like:

{'Great Customer': ['Great Customer LDA', 'Great Customer Enterprises','Great Customer Japan'],

'Best Customer': ['Best Offices', 'Best Customer LDA','BEST'],

}

Thanks in advance for your help!

Answer 1

IIUC, and building on @Leo's answer, would this work?

df = pd.DataFrame({'Customer Name': ['Great Customer','Great Customer','Great Customer','Best Customer','Best Customer','Best Customer'],
'Customer Alias': ['Great Customer LDA', 'Great Customer Enterprises', 'Great Customer Japan', 'Best Offices', 'Best Customer LDA', 'BEST']})
dfg = df.groupby('Customer Name').agg(list)
dict( zip( dfg.index, dfg["Customer Alias"]))

output

{'Best Customer': ['Best Offices', 'Best Customer LDA', 'BEST'],
 'Great Customer': ['Great Customer LDA',
  'Great Customer Enterprises',
  'Great Customer Japan']}