Strange DataFrame dtype inference behavior
#### Code

All the below are the same, I'm only going to vary the value in the 'c' column

```
>>> df = pd.DataFrame([[1, 2, '0.1']], columns=['a', 'b', 'c'])
>>> df.values
[[1 2 '0.1']]
>>> df.values[:,0]
[1]
>>> df.values[:,0].dtype
object
>>> df['a'].dtype
int64

>>> df2 = pd.DataFrame([[1, 2, 0.1]], columns=['a', 'b', 'c'])
>>> df2.values
[[ 1.   2.   0.1]]
>>> df2.values[:,0]
[ 1.]
>>> df2.values[:,0].dtype
float64
>>> df2['a'].dtype
int64

>>> df3 = pd.DataFrame([[1, 2, 3]], columns=['a', 'b', 'c'])
>>> df3.values
[[1 2 3]]
>>> df3.values[:,0]
[1]
>>> df3.values[:,0].dtype
int64
>>> df3['a'].dtype
int64
```

#### Problem description

In my use case, columns should have independent dtypes. A `1` is an integer, independent of what values are in other columns. This is obviously subjective, and profoundly important to pandas -- dtype inference is one of pandas' best attributes, but I was very surprised that it wasn't columnar-independent. 

I'd like to have some discussion about this, @jorisvandenbossche @jreback 

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 17.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.21.0
pytest: 3.6.1
pip: 10.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0b10
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
