How to get maximum and minimum of a list in column?

You can create DataFrame, then minimal and maximal values by DataFrame.agg, convert to lists and assign back if requirement is no loops (Apply are loops under the hood):

df = pd.DataFrame(dt.A.tolist())
dt['B'] = df.agg(['min','max'], axis=1).astype(int).values.tolist()
print (dt)
              A       B
0  [1, 2, 3, 4]  [1, 4]
1           [3]  [3, 3]
2     [2, 8, 4]  [2, 8]
3        [5, 8]  [5, 8]

If no problem with loops another solution with list comprehension, it should be faster like apply, depends of real data:

dt['B'] =  [[min(x), max(x)] for x in dt.A]

Just an alternative with explode:

dt['B'] = (dt['A'].explode().astype(int).groupby(level=0).agg(['min','max'])
           .to_numpy().tolist())
print(dt)

---

              A       B
0  [1, 2, 3, 4]  [1, 4]
1           [3]  [3, 3]
2     [2, 8, 4]  [2, 8]
3        [5, 8]  [5, 8]

Like this:

In [1592]: dt['B'] = dt.A.apply(lambda x: [min(x), max(x)])     
In [1593]: dt                                   
Out[1593]: 
              A       B
0  [1, 2, 3, 4]  [1, 4]
1           [3]  [3, 3]
2     [2, 8, 4]  [2, 8]
3        [5, 8]  [5, 8]

As suggested by @Ch3steR, using map since it's faster:

dt['B'] = dt.A.map(lambda x: [min(x), max(x)])