[FIXED] Python Pandas to group values in 2 columns

Issue

A data frame like below. the names are in 5 groups, linking by the common in column A.

I want to group the names. I tried:

import pandas as pd

data = {'A': ["James","James","James","Edward","Edward","Thomas","Thomas","Jason","Jason","Jason","Brian","Brian"], 
'B' : ["John","Michael","William","David","Joseph","Christopher","Daniel","George","Kenneth","Steven","Ronald","Anthony"]}
df = pd.DataFrame(data)

df_1 = df.groupby('A')['B'].apply(list)
df_1 = df_1.to_frame().reset_index()

for index, row in df_1.iterrows():
    print (row['A'], row['B'])

the outputs are:

('Brian', ['Ronald', 'Anthony'])
('Edward', ['David', 'Joseph'])
('James', ['John', 'Michael', 'William'])
('Jason', ['George', 'Kenneth', 'Steven'])
('Thomas', ['Christopher', 'Daniel'])

but I want one list for each group (it would be even better if there's an automatic way to assign a variable to each list)， like:

['Brian', 'Ronald', 'Anthony']
['Edward', 'David', 'Joseph']
['James', 'John', 'Michael', 'William']
['Jason', 'George', 'Kenneth', 'Steven']
['Thomas', 'Christopher', 'Daniel']

I tried row['B'].append(row['A']) but it returns None.

What's the right way to group them?

Solution

You can add values of A grouping column in GroupBy.apply with .name attribute:

s = df.groupby('A')['B'].apply(lambda x: [x.name] + list(x))
print (s)
A
Brian             [Brian, Ronald, Anthony]
Edward             [Edward, David, Joseph]
James      [James, John, Michael, William]
Jason     [Jason, George, Kenneth, Steven]
Thomas       [Thomas, Christopher, Daniel]
Name: B, dtype: object

Answered By - jezrael