Pandas concatenation of multiple dataframes returns null values

I have a dataframe (df), that I break down into 4 new dfs (media, client, code_type, and date). media has one column of null values, while the other three are only 1-dim dfs, each consisting of nulls. After replacing the nulls in each dataframe, I try to pd.concatto get a single df and get the result below.

clean = pd.concat([media, client, code_type, date], axis=1)  

    code    media       acq.    revenue   client code_type  date
0   RASH    Radio       50.0    34004.0     NaN     NaN     NaT
1   100     NaN         10.0    1035.0      NaN     NaN     NaT
2   NEWS    SiriusXM    61.0    3475.0      NaN     NaN     NaT
3   DR      SiriusXM    53.0    4307.0      NaN     NaN     NaT
4   SPORTS  SiriusXM    45.0    6503.0      NaN     NaN     NaT
5   DOUBL   Podcast     13.0    4205.0      NaN     NaN     NaT

clean.client is supposed to be all 2
clean.code_type should be all P
clean.date should be all 08/15/2016

The dfs by themselves show the data, it's only when I concatenate that I lose the information. I think it may be something with the indexes, but I'm not sure. Could also be something to do with the fact that I have a column with both str and int (see clean.code above) which might be why I get the runtime error listed below.

//anaconda/lib/python3.5/site-packages/pandas/indexes/api.py:71: RuntimeWarning: unorderable types: int() < str(), sort order is undefined for incomparable objects result = result.union(other)