groupby multiple values in a column

I have a subset of a dataframe here:

data = {'Name': ['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6'],
        'Time': [1,2,3,4,5,6],
        'Week' : [1, 2, 3, 2, 3, 2]
            }

dfx = pd.DataFrame(data)

I need to sum up all the times for each week so Week 1 time is 1, Week 2 time is 2+4+6, and Week 3 is 3+5. I also need it to look through the 'Week' column and find all the different weeks, so for this example there are 3 but for another dataframe it could be 2 or 4.

End result is look through a column in a dataframe, find the unique values (1,2,3,...n), groupby be each of those values into rows and sum up the time for each of those values.

I have tried a handful of ways but nothing is really working how I would like. I appreciate any help or ideas.

Expected Output:

                                 Sum
Week 1: 1                         1
Week 2: 2  4  6                   12
Week 3: 3  5                      8

The output can be either individual dataframes of the data or one dataframe that has all three rows with all the numbers and the sum at the end.

    import pandas as pd
data = {'Name': ['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6'],
        'Time': [1,2,3,4,5,6],
        'Week' : [1, 2, 3, 2, 3, 2]
            }

dfx = pd.DataFrame(data)
dfx = dfx.groupby('Week')['Time'].sum()
print(dfx)

output:

Week
1     1
2    12
3     8

You can groupby "Week", select column "Time", and you can pass multiple functions (such as list constructor and sum) to Groupby.agg to do the things you want:

out = dfx.groupby('Week')['Time'].agg(Times=list, Total=sum)

Output:

          Times  Total
Week                  
1           [1]      1
2     [2, 4, 6]     12
3        [3, 5]      8