[WIP] into(pytables Table, csv) with option to ignore errors in CSV files

This PR is for the use case of having a large quantity of CSV files with almost (but not entirely) perfect data. CSV reading is dispatched to `pd.read_csv` with the result appended to a pytables `Table`. Keyword argument flags for `read_csv` are propagated through the `into` call. In particular, something like this works:

```
tble = into(table, "some_ok_data.csv", error_bad_lines=False)
```
which gives the keyword argument ```error_bad_lines``` to ```read_csv``` as documented [here] (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html)
It provides some use as it is, but marked as WIP since it is probably more useful once it supports a glob of CSVs. Additionally, @mrocklin had some ideas about other ways to specify the pytables Table in the `into` call.
Suggest returning a `CSV` object rather than a Pandas DataFrame.  Then you could make an `into` implementation for `tb.Table <- CSV` by adding to the signature of the following implementation already in into

```Python
@dispatch((np.ndarray, pd.DataFrame, ColumnDataSource, ctable), CSV)
def into(a, b, **kwargs):
    return into(a, into(pd.DataFrame(), b), **kwargs)
```

Probably this function should also be improved by passing `kwargs` to the inner `into` call.

I know that this is a bit more round-about, but it also covers a lot more ground with `into`.  Generally we want to express each step with a separate `into` call if it is as efficient.  That way `into` is more generally useful in other cases.