Usefulness of error_bad_lines when dtypes are defined
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
from numpy import dtype

### Create the sample data
with open('data.csv', 'w+') as file:

    file.write("ID,X1,X2,X3\n")
    file.write("0,1,Amigo,3\n")
    file.write("1,1,Inimigo, amor,9\n")
    file.write("2,1,Cowboy,42\n") 
 
file.close()

dtypes = {"ID": dtype("int64"),
         "X1": dtype("int64"),
         "X2": dtype("O"),
         "X3": dtype("int64")}

print("Load df with no params: ", end="")
try:
    df = pd.read_csv("data.csv")
    print("Sucess")
except:
    print("Fail")

print("Load df with error bad lines: ", end="")
try:
    df = pd.read_csv("data.csv", error_bad_lines=False)
    print("Sucess")
except:
    print("Fail")

print("Load df with error bad lines and dtypes: ", end="")    
try:
    df = pd.read_csv("data.csv", error_bad_lines=False, dtype=dtypes)
    print("Sucess")
except:
    print("Fail")


```
#### Problem description

The problem is that `error_bad_lines` is pretty useful to deal with undesired commas inside the data that splits a single column into two new ones. But when `dtype` is defined, it checks the type of each column before skipping a problematic row, causing it to not match. 

I'd argue that the row should be skipped before checking the dtype, because when a problematic row appears its dtypes are messed.

#### Expected Output
It should skip the problematic row even when dtype is passed as param.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Darwin
OS-release: 17.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_BR.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: 3.0.5
pip: 9.0.1
setuptools: 36.5.0
Cython: None
numpy: 1.14.2
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.7.2
pytz: 2018.3
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 2.1.1
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.14
pymysql: None
psycopg2: 2.7.3.1 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: 0.1.4
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>

**update:** I've updated my pandas version.
