More consistent na_values handling in read_csv
The current handling of the `na_values` argument to `read_csv` is strangely different depending on what kind of value you pass to `na_values`.  If you pass None, the default NA values are used.  If you pass a dict mapping column names to values, then those values will be used for those columns, totally overriding the default NA values, while for columns not in the dict, the default values will be used.  If you pass some other kind of iterable, it uses the _union_ of the passed values _and_ the default values as the NA values.

This behavior is confusing because sometimes the passed values override the defaults, but other times they just add to the defaults.  It's also contrary to the documentation at http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files, which says: "If you pass an empty list or an empty list for a particular column, no values (including empty strings) will be considered NA."  But passing an empty list doesn't result in no values being considered NA.  In fact, passing an empty list does nothing, since the empty list is unioned with the default NA values, so the default NA values are just used anyway.

Currently there is no easy way to pass a list of NA values which overrides the default for all columns.  You can pass a dict, but then you have to specify the defaults per column.  If you pass a list, you're not overriding the defaults, you're adding to them.  This makes for confusing behavior when reading CSV files with string data in which strings like "na" and "nan" are valid data and should be read as their literal string values.

There should be a way to pass an all-column set of NA values that overrides the defaults.  One possibility would be to have two arguments, something like `all_na_values` and `more_na_values`, to specify overriding and additional values, respectively.  Another possibility would be to expose the default (currently the module-level `_NA_VALUES` in parsers.py), and allow users to add to it it they want to add more NA values (e.g., `read_csv(na_values=set(['newNA']) | pandas.default_nas)`.
