newcol = [..,..,.....]

df['colname'] = newcol

This will keep the colname intact while replacing its contents with newcol.

For me second solution working, but necessary convert strings to lists before:

import ast

df['Feedback'] = df['Feedback'].apply(ast.literal_eval)
#df['Feedback'] = df['Feedback'].str.strip('[]').str.split(',')

First solution working with nested dictionary:

df = df.assign(Feedback=[[feedback_dict.get(i,i) for i in x] for x in df['Feedback']])


df['Feedback'] = df['Feedback'].apply(lambda x : [feedback_dict.get(i,i)  for i in list(x)])
print (df)
     ID    Feedback
0  T223  [1, 3, 3]
1  T334  [2, 1, 1]

EDIT: If instead lists are missing values use if-else statement - non list values are replaced to empty lists:

print (df)
     ID             Feedback
0  T223       [Good,Bad,Bad]
1  T334  [Average,Good,Good]
2   NaN                  NaN


feedback_dict = {'Good':1, 'Average':2, 'Bad':3}
df = df.assign(Feedback=[[feedback_dict.get(i,i) for i in x] if isinstance(x, list) else [] 
                            for x in df['Feedback']])

print (df)
     ID   Feedback
0  T223  [1, 3, 3]
1  T334  [2, 1, 1]
2   NaN         []

There is one way using astype , Even it work , but I still highly recommend you using cold's answer.

df.astype(str).replace({'[4, 5]':4.5,"['apple', 'pear']":"apple"})
Out[159]: 
       a
0      1
1      2
2      3
3    4.5
4  apple

If you don't mind getting a new data frame object returned as opposed to updating the original Pandas .assign() will avoid SettingWithCopyWarning. Your example:

df = df.assign(B=df1['E'])