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.

<!---
@huboard:{"order":496.0,"milestone_order":496,"custom_state":""}
-->

I think creating a `'blaze.h5'` file in the user's current directory is dangerous. How about a temporary file with `blaze` somewhere in the name?

```python
fname = kwargs.get('output_path', tempfile.mkstemp(suffix='blaze'))
```
Here you'd get an empty file if `'output_path' not in kwargs` otherwise you're writing to a guaranteed unique file name. You can also check for the existence of `'output_path' in kwargs` and only generate the file if that tests `False`