python: cumulative concatenate in pandas dataframe

Question

How to do a cumulative concatenate in pandas dataframe? I found there are a number of solutions in R, but can't find it in python.

Here is the problem: suppose we have a dataframe: with columns: date and name :

import pandas as pd

d = {'date': [1,1,2,2,3,3,3,4,4,4], 'name':['A','B','A','C','A','B','B','A','B','C']}
df = pd.DataFrame(data=d)

I want to get CUM_CONCAT , which is a cumulative concatenate groupby date:

    date name  CUM_CONCAT
0     1    A      [A]
1     1    B      [A,B]
2     2    A      [A]
3     2    C      [A,C]
4     3    A      [A]
5     3    B      [A,B]
6     3    B      [A,B,B]
7     4    A      [A]
8     4    B      [A,B]
9     4    C      [A,B,C]

so far i've tried:

temp = df.groupby(['date'])['name'].apply(list)
df = df.join(temp, 'date', rsuffix='_cum_concat')

and what i've got was:

    date name  CUM_CONCAT
0     1    A      [A,B]
1     1    B      [A,B]
2     2    A      [A,C]
3     2    C      [A,C]
4     3    A      [A,B,B]
5     3    B      [A,B,B]
6     3    B      [A,B,B]
7     4    A      [A,B,C]
8     4    B      [A,B,C]
9     4    C      [A,B,C]

I know there are .rolling and cumsum functions, which are similar to what i need, but they are mainly for cumulative sum not for concat.

Any help will be appreciated!!!

Answer 1

pandas rolling will not support object , so you may need

df['CUM_CONCAT']=[y.name.tolist()[:z+1] for x, y in df.groupby('date')for z in range(len(y))]
df
Out[33]: 
   date name CUM_CONCAT
0     1    A        [A]
1     1    B     [A, B]
2     2    A        [A]
3     2    C     [A, C]
4     3    A        [A]
5     3    B     [A, B]
6     3    B  [A, B, B]
7     4    A        [A]
8     4    B     [A, B]
9     4    C  [A, B, C]

Answer 2

I have came up with a solution as follow:

In terms of time taken to run, both solutions (me and @Wen-Ben) seem similar, his code is shorter tho

from itertools import accumulate

def cum_concat(x):
    return list(accumulate(x))
f = lambda x: cum_concat([[i] for i in x])
b =df.groupby(['date'])['name'].apply(f)
df['CUM_CONCAT']=[item for sublist in b for item in sublist]

df
Out: 
   date name CUM_CONCAT
0     1    A        [A]
1     1    B     [A, B]
2     2    A        [A]
3     2    C     [A, C]
4     3    A        [A]
5     3    B     [A, B]
6     3    B  [A, B, B]
7     4    A        [A]
8     4    B     [A, B]
9     4    C  [A, B, C]