Bug: Conversion to series with datetime items
#### Code Sample, a copy-pastable example if possible

```python
# Setup
import pandas as pd
dates = pd.to_datetime(range(1,300), unit='D', origin=pd.Timestamp('2000-01-01'))
se = pd.Series(range(1,300),dates)
df = pd.DataFrame(se).reset_index()

# The problem:
>>> df
         index    0
0   2000-01-02    1
1   2000-01-03    2
2   2000-01-04    3
3   2000-01-05    4
4   2000-01-06    5
5   2000-01-07    6
..         ...  ...
294 2000-10-22  295
295 2000-10-23  296
296 2000-10-24  297
297 2000-10-25  298
298 2000-10-26  299

[299 rows x 2 columns]
>>> pd.Series(df['index'], index = df[0])
0
1     2000-01-03
2     2000-01-04
3     2000-01-05
4     2000-01-06
5     2000-01-07
         ...
295   2000-10-23
296   2000-10-24
297   2000-10-25
298   2000-10-26
299          NaT
Name: index, Length: 299, dtype: datetime64[ns]
```

#### Problem description

The commands above should not generate any NaT.

In a real life case, I tried to do the same thing with 56355 rows and had 53913 NaT at the end.

#### Expected Output

No NaT
My current work around is: `df.set_index('0')`


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

<details>
>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.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: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.13.0
scipy: 0.19.0
xarray: None
IPython: 6.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.6
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
