In Python, I load a couple of csv files, each file having more than 100 columns into a Pandas' data frame. Panda's data-frame, in short is a tabular structure which lets me performs on bunch of joins, and filter out data. I generated different shapes of reports using these operations. Of course, I would love some type checking to help me with these merge, join operations as I create different reports.
 
I am not looking to replicate the Pandas data-frame functionality in Haskell. First thing I want to do is reach out to the 'record' data structure. Here are some ideas I have:

1.  I need to declare all these 100+ columns into multiple record structures.

2.  Some of the columns can have NULL/NaN values. Therefore, some of the attributes of the record structure would be 'MayBe' values. Now, I could drop some columns during load and cut down the number of attributes i created per record structure.

3.  Create a dictionary of each record structure which will help me index into into them.'

I would like some feedback on the first 2 points. Seems like there is a lot of boiler plate code I have to generate for creating 100s of record attributes. Is this the only sane way to do this?  What other patterns should I consider while solving such a problem. 

Also, I do not want to add too many dependencies into the project, but open to suggestions.

Any input/advice on this would be very helpful.