Append two Pandas series to a dataframe by columns

Question

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().

Answer 1

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

                      cc        ac
2017-01-01 00:00:00  0.0 -0.319653
2017-01-01 01:00:00  0.0  0.630061
2017-01-01 02:00:00  0.0 -1.648402
2017-01-01 03:00:00  0.0 -1.141017
2017-01-01 04:00:00  0.0 -0.643353
2017-01-01 05:00:00  0.0  0.718771
2017-01-01 06:00:00  0.0  0.379173
2017-01-01 07:00:00  0.0  1.799804
2017-01-01 08:00:00  0.0  0.883260
2017-01-01 09:00:00  0.0  0.788289
2017-01-01 10:00:00  0.0       NaN
2017-01-01 11:00:00  0.0       NaN
2017-01-01 12:00:00  0.0       NaN
2017-01-01 13:00:00  0.0       NaN
2017-01-01 14:00:00  0.0       NaN
2017-01-01 15:00:00  0.0       NaN
2017-01-01 16:00:00  0.0       NaN
2017-01-01 17:00:00  0.0       NaN
2017-01-01 18:00:00  0.0       NaN
2017-01-01 19:00:00  0.0       NaN
2017-01-01 20:00:00  NaN       NaN
2017-01-01 21:00:00  NaN       NaN
2017-01-01 22:00:00  NaN       NaN
2017-01-01 23:00:00  NaN       NaN
2017-01-02 00:00:00  NaN       NaN