text: Title: df support
  Body: Here are the things we need to make sure df support includes:
  - [x] splitting out the output column
  - [x] convert a list of dictionaries to a DataFrame (ideally with error logging to the user)
  - [x] convert numbers stored as strings to proper numbers
  - [x] convert numbers stored as strings with commas in them to proper numbers. 
  - [x] dropping all rows where the output column is an ineligible value (nan, None, etc.). this particularly comes into play for our subpredictors. 
  - [x] ignoring (dropping) any columns that should be ignored
  - [x] date feature engineering
  - [x] robust feature scaling
  - [x] optional (gscv) feature truncating
  - [x] removing all nan, None, and other values
  - [ ] FUTURE: imputing missing values
  - [x] getting dummies from categorical columns
  - [x] convert to a scipy sparse matrix
  - [x] support NLP feature transformations. For now we'll just do it as-is, but in the future, we'll probably split this out into it's own thing that we'l do separately, and just hstack the sparse results from tfidfvectorizer onto the sparse results from vectorizing our dataframe. 
  