You’d have to do:
df['new_col'] = [my_list] * len(df)
Example:
In [13]:
df = pd.DataFrame(np.random.randn(5,3), columns=list('abc'))
df
Out[13]:
a b c
0 -0.010414 1.859791 0.184692
1 -0.818050 -0.287306 -1.390080
2 -0.054434 0.106212 1.542137
3 -0.226433 0.390355 0.437592
4 -0.204653 -2.388690 0.106218
In [17]:
df['b'] = [[234]] * len(df)
df
Out[17]:
a b c
0 -0.010414 [234] 0.184692
1 -0.818050 [234] -1.390080
2 -0.054434 [234] 1.542137
3 -0.226433 [234] 0.437592
4 -0.204653 [234] 0.106218
Note that dfs are optimised for scalar values, storing non scalar values defeats the point in my opinion as filtering, looking up, getting and setting become problematic to the point that it becomes a pain