RFE: a faster way to construct a pandas.Timestamp from an epoch time
`pandas.to_datetime` called with an `int` is too slow for my use case. Basically, I have a loop that sequentially gets an integer from a generator of about 1 000 000 numbers, converts it to `pandas.Timestamp` and passes it to a function. A profiler says that the call of `pandas.to_datetime` takes about 40 % of the total run time of my program.

Compared to `datetime.datetime.fromtimestamp`, it's more than 60 times slower:
```console
$ python -m timeit -n 1000000 -s 'import datetime' 'datetime.datetime.fromtimestamp(30, tz=datetime.timezone.utc)'
1000000 loops, best of 3: 0.889 usec per loop
$ python -m timeit -n 1000000 -s 'import pandas' 'pandas.to_datetime(30, utc=True, unit="s")'
1000000 loops, best of 3: 62.8 usec per loop
```

<details>

```console
$ python -c 'import pandas;pandas.show_versions()'

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
</details>

Can you please provide/document a faster way to instantiate a `pandas.Timestamp` instance from an epoch time?