to_datetime followed by update on column ruins data type and formatting
Before transformation of birthday entries stupidly recorded as floating-point values by company partner:
```
0            NaN
1    1.98508e+07
2    1.98811e+07
3            NaN
4            NaN
5            NaN
6            NaN
7            NaN
8     1.9951e+07
9    1.99605e+07

```
After I apply the following code to non-null values:
```python
In:
pd.to_datetime(df.birthday[~df.birthday.isnull()].astype(int).astype(str), format="%Y%m%d")

Out:
1         1985-08-11
2         1988-11-07
8         1995-10-01
9         1996-05-03
15        1986-05-19
16        1996-06-19
17        1995-06-18
27        1994-04-01
29        1997-08-22
30        1990-08-04
```

Great, exactly what I want. But, when I go to update the dataframe with these lovely date values:
`df_backup.birthday.update(pd.to_datetime(df.birthday[~df.birthday.isnull()].astype(int).astype(str), format="%Y%m%d"))`, the birthday values in the dataframe become:

```
0                         NaN
1          492566400000000000
2          594864000000000000
3                         NaN
4                         NaN
5                         NaN
6                         NaN
7                         NaN
8          812505600000000000
9          831081600000000000
```

It occurred to me that this may be happening because the column I'm updating is a float-type column, and so the datetime objects are awkwardly coerced to floats, but then, how to allow for the presence of NaNs and datetime objects in this column? I tried first changing the birthday column to type object before updating with datetime objects, but that didn't solve the issue. I will try the workaround of left joining as a new column.

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.8.2
pip: 18.0
setuptools: 40.4.3
Cython: 0.28.5
numpy: 1.15.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.8
xlrd: None
xlwt: None
xlsxwriter: 1.1.1
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.12
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
