Append two Pandas series to a dataframe by columns

I have a dataframe and two Pandas Series ac and cc, i want to append this two series as column. But the problem is that my dataframe has a time index and Series as integer

A='a'

cc  = pd.Series(np.zeros(len(A)*20))
ac  = pd.Series(np.random.randn(10))

I try this but I had an empty dataframe

index = pd.date_range(start=pd.datetime(2017, 1,1), end=pd.datetime(2017, 1, 2), freq='1h')

df = pd.DataFrame(index=index)

df = df.join(pd.concat([pd.DataFrame(cc).T] * len(df), ignore_index=True))
df = df.join(pd.concat([pd.DataFrame(ac).T] * len(df), ignore_index=True))

The final result should be something like this :

                    cc    ac   
2017-01-01 00:00:00   1    0.247043 
2017-01-01 01:00:00   1    -0.324868 
2017-01-01 02:00:00   1    -0.004868
2017-01-01 03:00:00   1    0.047043 
2017-01-01 04:00:00   1    -0.447043 
2017-01-01 05:00:00 NaN    NaN 
...                 ...    ...

It's not a problem if we always have NaN in the final result.

EDIT:

After the answer of @piRSquared , i have to add a loop but i got an error in the keys :

az = [cc, ac]

for i in az:
    df.join(
            pd.concat(
            [pd.Series(s.values, index[:len(s)]) for s in [i]],
            axis=1, keys=[i]
           )
         )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().