PERF: Apply on Series much slower than DataFrame when the function is related with scipy.stats.norm
#### Code Sample, a copy-pastable example if possible

```
%%timeit
pd.Series([1.0]*1000).apply(lambda x: x**2)
771 µs ± 15.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%%timeit
pd.DataFrame([1.0]*1000).apply(lambda x: x**2)
1.8 ms ± 15.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%%timeit
pd.Series([1.0]*1000).apply(norm.pdf)
146 ms ± 3.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%%timeit
pd.DataFrame([1.0]*1000).apply(norm.pdf)
2.08 ms ± 27.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
#### Problem description

Normally my guess would be that Series should work better than DataFrame or at least the same, if the data is the same, like what I presented using x**2 (Point it out if I am wrong). However, when it comes to scipy.stats.norm, something strange happens.

#### Expected Output

Time cost is similar for Series and DataFrame

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 2008ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.13.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

​

</details>

