如何根据另一列的行值查看另一列的数据？

Question

I have a dataframe:

data = [['Alex',10],['Alex',11],['Alex',8],['Bob',12],['Bob',14],['Clarke',13]]
df2 = pd.DataFrame(data,columns=['Name','Age'])

I want to print the age values for unique values of Names. For example, I want to print all age values for the name 'Alex' and so on. I tried parsing through the Name values, which are unique, so in the end I would print:

Alex: 10,11,8
Bob: 12,14
Clarke: 13

How can I print the age values for each unique Name of the dataframe?

for name in df2['Name'].unique():
  print(name)
  print(df2['Age'])

Answer 1

You can try this

unique = df2.groupby(by='Name')['Age'].apply(list)
for i in unique.iteritems():
    print(i)

output

('Alex', [10, 11, 8])
('Bob', [12, 14])
('Clarke', [13])

Answer 2

Building upon Ade_1's answer above, you can format the output to match your requirements with the following code:

unique = df2.groupby(by='Name')['Age'].apply(list)
for name in unique.index:
    ages = str(unique[name]).strip('[]')
    print('{}: {}'.format(name, ages))

The output will be:

Alex: 10, 11, 8
Bob: 12, 14
Clarke: 13