You can use the str accessor for some list operations as well. In this example,
df['CreationDate'].str.len()
returns the length of each list. See the docs for str.len.
df['Length'] = df['CreationDate'].str.len()
df
Out:
CreationDate Length
2013-12-22 15:25:02 [ubuntu, mac-osx, syslinux] 3
2009-12-14 14:29:32 [ubuntu, mod-rewrite, laconica, apache-2.2] 4
2013-12-22 15:42:00 [ubuntu, nat, squid, mikrotik] 4
For these operations, vanilla Python is generally faster. pandas handles NaNs though. Here are timings:
ser = pd.Series([random.sample(string.ascii_letters,
random.randint(1, 20)) for _ in range(10**6)])
%timeit ser.apply(lambda x: len(x))
1 loop, best of 3: 425 ms per loop
%timeit ser.str.len()
1 loop, best of 3: 248 ms per loop
%timeit [len(x) for x in ser]
10 loops, best of 3: 84 ms per loop
%timeit pd.Series([len(x) for x in ser], index=ser.index)
1 loop, best of 3: 236 ms per loop