pd.read_table: Using space as delimiter on file with trailing space gives cryptic error

```python
import numpy as np
import os
import pandas as pd

# put test.csv in same folder as script
mydir = os.path.dirname(os.path.abspath(__file__))
csv_path = os.path.join(mydir, "test.csv")

df = pd.read_table(csv_path, sep=' ',
                   comment='#',
                   header=None,
                   skip_blank_lines=True,
                   names=["A", "B", "C", "D", "E", "F", "G"],
                   dtype={"A": np.int32,
                       "B": np.int32,
                       "C": np.float64,
                       "D": np.float64,
                       "E": np.float64,
                       "F": np.float64,
                       "G": np.int32})
```
`test.csv`:
```csv
2270433 3 21322.889 11924.667 5228.753 1.0 -1 
2270432 3 21322.297 11924.667 5228.605 1.0 2270433 
```

#### Problem description

Attempting to load test.csv with pd.read_table() results in the following errors: 
`TypeError: Cannot cast array from dtype('float64') to dtype('int32') according to the rule 'safe'`

and

`ValueError: cannot safely convert passed user dtype of int32 for float64 dtyped data in column 2`

Expected behavior:

Either trailing whitespace is ignored by Pandas, or throw a more informative error than "cannot safely convert passed user dtype of int32 for float64". It took me a really long time to figure out that this was caused by trailing spaces in the csv.
