datetools.parse interface
- [ ] document in docstring / cookbook / timeseries.rst usage of combing integer columns into YYYYMMDD and parsing to datetimes
- [ ] clean up imports of `_parse/parse` from dateutils
- [ ] `to_period`  to create `PeriodIndex`

I started to make a PR #5885 to fix what I thought was a typo before realizing that this was intentional. It still doesn't make much sense to me though why I would want this return of datetime, _result, resolution. Maybe the whole approach could use a refactor. Otherwise what am I missing?

My typical use case for datetools.parse is something like

`dates = map(lambda x : parse('  '.join(x)), zip(df.day, df.month, df.year))`

A couple of questions.
1. Is there a better way to do this vectorized to datetime operation? AFAICT pd.to_datetime doesn't actually use the 'advanced' parsing for quarterly and monthly dates.
2. Should this all be unified? Assuming I haven't missed it, should there be, e.g., a function `pd.parse_dates` that is a general parser for both strings and works on array-like input, deprecating datetools.parse, datetools.parse_time_string, and datetools.to_datetime. This function could also have a flag to return Period or TimeStamp objects with frequency information instead of the current return of the parsed object and resolution. Given that I'm having to do things like

`dates = [x[0] for x in map(lambda x : parse('  '.join(x)), zip(df.day, df.month, df.year))]`

Thoughts?
