ENH GH20927 Added convert_dates parameter (default=True) to read_sas()
Added parameter which is passed down to the underlying
SAS7BDATReader.  This allows SAS datasets with dates too large
for pandas.datetime to be read in with the dates/datetimes as
SAS native float64 values, rather than trying (and failing) to convert
to pandas.datetime.

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

Could these be converted to python datetime? > Could these be converted to python datetime?

@jbrockmendel : I suspect you'll have to dig into the `SAS7BDATReader` internals to find out more.  I think the change as it stands makes sense. re: Could these be converted to python datetime?

@jbrockmendel, @gfyoung:
Thanks for comment and review.

I did initially look to catch the exception and convert to datetime.datetime instead of Pandas datetime, however it breaks some existing tests so I thought it simpler to expose the convert_dates parameter from SAS7BDATReader and allow the user to explicitly deal with the very large dates.
I'm happy to look further into the conversion to python datetime if you think this is a better solution (and have code I can submit if it is of interest) @paul-lilley : In the interests of modularity, I would not make any changes to `SAS7BDATReader` for this PR.  You are more than welcome to investigate and submit a PR for the `SAS7BDATReader` if you would like following this one. @jreback 
Hi, thanks for the review.  
Just to clarify, do you mean to keep the current conversion, returning pd.datetime if all ok, catch OutOfBoundsDatetime if not and return an object array of datetime.datetime objects? 
This would return different types depending on the range of dates in the SAS dataset, as SAS7BDATReader is an iterator this could give some chunks converted to pd.datetime and others to datetime.datetime within a given SAS dataset, but I guess this will unlikely break any existing code and retains the more efficient pd.datetime (ie. a numpy datetime) for most SAS datasets.
 > https://github.com/pandas-dev/pandas/pull/21983#issuecomment-407642117

@paul-lilley yes that's what I would do. This is how pandas works elsewhere and would be least suprising.'
Please update the title of the PR as well. Thanks - I'll make the changes and update the PR title, probably after Europython this week. can you merge master and update to comments closing as stale. if you'd like to continue, pls ping. I'll pick this up in a new PR, resolving the issue as discussed with jreback above on 25 Jul 2018