read_csv silently passes through rows with too many columns when chunking
I stumbled upon this today while working with the chunking feature. I was trying to implement a CSV cleaner that would set aside error rows (or a chunk that has an error) for future review.

Given input table as 'test.csv':

```
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4
1,2,3,4,5,6,7,8
1,2,3,4
1,2,3,4
```

And reading it with chunking...

```
df = pd.read_csv(
    'test.csv',
    header=None,
    chunksize=1,
    error_bad_lines=True  
)

for chunk in df:
    print(chunk)
```

...results in:

```
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
   0  1  2  3
0  1  2  3  4
```

...silently truncating row 7.

It does the expected if the error row is in the first chunk. But error rows in subsequent chunks are silently truncated. I assume this is not expected behavior -- correct me if I'm wrong.

I'd love to tackle this. But I've never contributed to this codebase. I figured I'd raise it first before diving in headfirst.
