Merge values in columns into one column - python-3.x

Question

I have a dataframe with columns and I'd like to group the ones starting with 'Answer' into one named 'Answers'. This column already exists, but around line 3061 there are no more values, I have to add them. I've tried that so far:
columns_with_answer = [col for col in df if col.startswith('Answer')]
df['Answers']= df.columns_with_answer.tolist()
But I got:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-399b932d5740> in <module>()
1 columns_with_answer = [col for col in df if col.startswith('Answer')]
----> 2 df['Answers']= df.columns_with_answer.tolist()
/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py in __getattr__(self, name)
5272 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5273 return self[name]
-> 5274 return object.__getattribute__(self, name)
5275
5276 def __setattr__(self, name: str, value) -> None:
AttributeError: 'DataFrame' object has no attribute 'columns_with_answer'
So, with the sample data:
>>>import numpy as np
>>>df = pd.DataFrame({'A':list('abcdefg'),'B':[4,5,4,5,5,4, np.nan],'Answer1':['a','b','d',np.nan,'d',np.nan,'f'],'Answer2':['a','b','d','e','h','d','k'],'Answer3':['a','b',np.nan,'d','r',np.nan, 'l'],'F':list('aaabbbc'),'Answers':['truc', 'machin', 'bidule', np.nan,np.nan,np.nan,np.nan] })
>>>df.head()
A B Answer1 Answer2 Answer3 F Answers
0 a 4 a a a a [truc]
1 b 5 b b b a [machin]
2 c 4 d d NaN a [bidule]
3 d 5 NaN e d b nan
4 e 5 d h r b nan
I would like to start from row 3 to get:
A B Answer1 Answer2 Answer3 F Answers
0 a 4 a a a a [truc]
1 b 5 b b b a [machin]
2 c 4 d d NaN a [bidule]
3 d 5 NaN e d b [nan, e, d]
4 e 5 d h r b [d, h, r]

Answer 1

Select values by list [] and then convert to numpy array before converting to list:
df['Answers']= df[columns_with_answer].to_numpy().tolist()
Or use DataFrame.filter with regex parameter and ^ for start of string:
df['Answers']= df.filter(regex='^Answer').to_numpy().tolist()
EDIT: For apply solution only for rows if Answers column is filled missing values use:
columns_with_answer = [col for col in df if col.startswith('Answer') and col != 'Answers']
mask = df['Answers'].isna()
print (mask)
0 False
1 False
2 False
3 True
4 True
5 True
6 True
Name: Answers, dtype: bool
L = df.loc[mask, columns_with_answer].to_numpy().tolist()
df.loc[mask, 'Answers'] = pd.Series(L, index=df.index[mask])
print (df)
A B Answer1 Answer2 Answer3 F Answers
0 a 4.0 a a a a truc
1 b 5.0 b b b a machin
2 c 4.0 d d NaN a bidule
3 d 5.0 NaN e d b [nan, e, d]
4 e 5.0 d h r b [d, h, r]
5 f 4.0 NaN d NaN b [nan, d, nan]
6 g NaN f k l c [f, k, l]