Implement vectorized, NA-friendly friendly string utils, a la R's stringr

[wesm]

cc @hammer, @arthurgerigk. Not sure when will be able to make this happen, but this would be a very nice addition. I've often found myself doing stuff like:

df[col].map(lambda x: x[:10]) 

or various other forms of string munging / regex-processing.

Obviously that would fail if any of df[col] is NA. And having to write this kinda sucks:

df[col].map(lambda x: x[:10] if notnull(x) else x) 

If multiple columns were involved in some kind of string processing exercise, you'd just want the whole operation to short circuit and be NA if an NA is encountered.

[wesm]

from @gdraps in #746

Hi Wes,

First off, thanks for Pandas and your recent talk at the NYC Python meetup.  On the topic of filtering, I found that NumPy vector filters are awesome for numeric data, but I have found myself reaching for the following idioms when dealing with alpha-numeric columns:

   df[df.method.contains('abc')]
   df[df.method.startswith('ghi')]
   df[df.method.endswith('xyz')]

Would you consider the addition of these methods to the Series class, not only to complement the existing `isin()` method, but to bridge the gap with SQL libraries, such as SQLAlchemy (http://docs.sqlalchemy.org/en/latest/core/expression_api.html#sqlalchemy.sql.operators.ColumnOperators), and improve expressiveness of string queries in Pandas?

[wesm]

Better late than never. Excited to finish this feature