Support for missing data and pandas dataframes

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It would be very useful for pysal to recognize and handle NaN values in NumPy 
arrays and/or pandas dataframes. Sometimes, it is not desirable to simply drop 
all observations with missing data, as these observations can be important when 
calculating spatial lags. 

Related, it would also be helpful to use pandas indexing to align the spatial 
weights matrix or matrices with the variables. Again, this is primarily an 
issue because of missing data.

Thanks!

Original issue reported on code.google.com by ada...@ucsc.edu on 31 Jan 2013 at 8:49

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To the pysal devs, we have this support already in statsmodels. We really need 
to find some time to think about combining the libraries, or at least making it 
so you can leverage our general "framework" code and we can provide support for 
the spatial weights in statsmodels to use all of your work. It doesn't make 
much sense for us to solve all of the same problems twice.

Original comment by jsseab...@gmail.com on 1 Feb 2013 at 1:47

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Totally agreed. Do you have any time Friday mornings 9mst which is when we have 
dev meetings via google hangouts. If so we could dedicate an upcoming one to 
start on the discussions. 

Original comment by sjsrey on 1 Feb 2013 at 3:58

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I'm pretty thin until March most likely but would be interested to set 
something up then. I had a good look through a decent amount of pysal over the 
summer after we spoke to see where we could combine. I have some thoughts on 
this but not a lot of time to devote to it at the moment (busy dissertating and 
chasing a few measly dollars).

Original comment by jsseab...@gmail.com on 5 Feb 2013 at 10:12