DataFrame.apply() raises ValueError when output df is different size than input df
#### MWE

```python
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(10, 2))  # dummy array
df1 = df.apply(np.fft.fft, axis=0)  # works
print(df1.shape)  # for testing
df2 = df.apply(np.fft.rfft, axis=0)  # breaks
print(df2.shape)  # for testing
```
#### Problem description

I would like to take a DataFrame of time series and apply the real-fft along the columns, but it seems that DataFrame.apply only works if the function to be applied returns output that is the same size as the input.

#### Expected Output
I expect the code block above to run without error and produce the following output:
```python
(10, 2)
(6, 2)
```
Alternatively, raising an error that tells the user "output array size must match input array size" would be fine, if we want to restrict apply to working only for functions that return the same-size arrays as the inputs.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None

pandas: 0.20.2
pytest: 3.2.1
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.0
xarray: 0.9.6
IPython: 6.1.0
sphinx: 1.6.2
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
