Inconsistent date parsing with read_csv
#### Code Sample, a copy-pastable example if possible

```
> cat events.csv
id;name;date
1;event1;11/03/2011
2;event2;03/05/2011
3;event3;20/10/2011
4;event4;04/08/2011
5;event5;05/10/2011
6;event6;17/11/2011
7;event7;11/02/2011
8;event8;22/02/2011
9;event9;29/04/2011
```

```python
import pandas as pd
pd.read_csv('events.csv', index_col=0, delimiter=';', parse_dates=[2])
```

```
      name       date
id
1   event1 2011-11-03
2   event2 2011-03-05
3   event3 2011-10-20
4   event4 2011-04-08
5   event5 2011-05-10
6   event6 2011-11-17
7   event7 2011-11-02
8   event8 2011-02-22
9   event9 2011-04-29
```
#### Problem description
Hello everyone. The main reason I am submitting this as a bug is because I spent about 2 hours conducting wrong analysis because of the behaviour I explain below.

As you can see above, the date column in the CSV file is in DD/MM/YYYY format. Now I know there exists a `dayfirst` flag, but here is the thing: the bahaviour is not consistent over the total rows that are being parsed. With the `dayfirst` flag set to `False`, some rows are parsed as being in MM/DD/YYYY format (e.g. rows 1 and 2), while others are parsed as being in DD/MM/YYYY format (e.g. rows 3 and 6). This is because rows 3 and 6 have dates where the day number is >12 and thus they are parsed as DD/MM/YYYY.

I understand that many people will find this a minor issue and maybe it is, because there are ways to overcome this, but in my opinion the illustrated behaviour is wrong and counter-intuitive, because it hides under the hood the fact that dates are parsed per row, when the elements of a column of a `DataFrame` supposedly have a uniform data type.

So I think that `pandas` should at least give the user a warning about that (the easy way) or try to apply consistent parsing among the rows (the hard and possibly slow way).

#### Expected Output
Either infer the date format and apply uniform parsing over all rows or show a warning that some rows are being parsed with a different date format in mind.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 17.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8

pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: None
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: None
matplotlib: 2.1.1
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
