performance of Series label-indexing with a list of labels
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
from numpy import random
dct = dict(zip(range(1000), random.randint(1000, size=1000)))
keys = random.randint(1000, size=1000000).tolist()
%timeit [dct[k] for k in keys]
# 86.2 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
sdct = pd.Series(dct)
%timeit sdct[keys]
# 673 ms ± 10 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
#### Problem description

I would expect the Series performance to be comparable, if not faster than the Python comprehension.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 35.0.2
Cython: None
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.6.1
xarray: None
IPython: 6.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.2
matplotlib: 2.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: None
pandas_datareader: None
</details>