pandas中实现将相同ID的字符串进行合并

"""
id   value
1      A
1      B
1      C
2      D
2      E
2      F
变为：
id  value
1   [A,B,C]
2   [D,E,F]

pandas怎么把上面的结构变为下面的形式？
"""

import pandas as pd
import numpy as np
data = pd.DataFrame({'id':[1,1,1,2,2,2],'value':['A','B','C','D','E','F']})

data1 = np.array(data.groupby(['id']))#按照id进行分类
# 转变成array对象之后，可以根据data1[0][1]查看其结构

id_kinds = 2#id的种类，此例比如1、2共两种
all_value = []
for j in range(2):
    value = []
    for i in data1[j][1]['value']:
        value.append(i)
    all_value.append(value)
print(all_value)

#再创建新的dataframe
new_data = pd.DataFrame({'id':[1,2],'value':all_value})
print(new_data)

import pandas as pd
import numpy as np
data = pd.DataFrame({'id':[1,1,1,2,2,2],'value':['A','B','C','D','E','F']})
data1 = data.groupby(by='id')['value'].sum()

data = pd.DataFrame({'id':[1,1,1,2,2,2],'value':['A','B','C','D','E','F']})
data['value'] = data['value'].apply(lambda x:','+ x)

data1 = data.groupby(by='id').sum()

data1['value'] = data1['value'].apply(lambda x :[x[1:]])

data1 = data.groupby(by='id').apply(lambda x:[','.join(x['value'])])

for ID，group in group_df:
    print(ID)
    print(group)

a = ['a','b']
c = pd.Series(a).sum()

data['value'].apply(lambda x :*****)
data.groupby(by='**').apply(lambda x :*****)