`apply` to multiple columns consumes a lot of memory
I was encouraged to post here after asking a [question on stackoverflow](https://stackoverflow.com/questions/35911319/pandas-apply-into-multiple-columns-consuming-lots-of-memory).
#### Code Sample, a copy-pastable example if possible

```
import numpy as np
import pandas as pd

# placeholder for real function
def process(x):
    return np.random.rand(100)

s = pd.Series(range(2700000))
df = s.apply(lambda x: pd.Series(process(x)))
```
#### Expected Output

Never finishes on my machine, as it consumes in excess of 16GB of RAM.

If I instead write `df = s.apply(lambda x: process(x))` then it finishes quite quickly and without consuming that much memory, but in an incorrect layout for my use. I would expect the above to perform close to the same, as the only difference is in the layout of the dataframe.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.3-300.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_DK.UTF-8

pandas: 0.17.1
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.9.2
scipy: 0.14.1
statsmodels: None
IPython: 3.2.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 0.6.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
