pd.concat on two (or more) series produces all-NaN dataframe
this is from a stackoverflow question [here](http://stackoverflow.com/questions/32441403/pandas-concat-produces-all-nan), you can download serialized object [here](https://www.evernote.com/l/AH4AdfgOJJROuZSfGfDR_jZvA0zEpIHgyq0) and reproduce using **python 2.7** and **pandas 0.16.2**.

I'm trying to concat two series with multiindex using `pd.concat([a, b], axis=1)` like so:

``` python
>>>payed_orders.head()
dt          product_id
2015-01-15  10001          1
            10007          1
            10016         14
            10022          1
            10023          1
Name: payed_orders, dtype: int64

>>>refund_orders.head()
dt          product_id
2015-01-15  10007         1
            10016         4
            10030         1
2015-01-16  10007         3
            10008         1
Name: refund_orders, dtype: int64

>>>pd.concat([payed_orders.head(), refund_orders.head()], axis=1, ignore_index=False)
        payed_orders    refund_orders
dt  product_id      
2015-01-15  10001   NaN NaN
            10007   NaN NaN
            10016   NaN NaN
            10022   NaN NaN
            10023   NaN NaN
            10030   NaN NaN
2015-01-16  10007   NaN NaN
            10008   NaN NaN
```

I've checked the index type and many other stuff to make sure no obvious were made, I've read the docs to learn that concatenating and merging series and dataframes may introduce `NaN`, but I didn't find anything in the docs to explain this behavior.
