title: Python Machine Learning - Imputing categorical data?
body: <p>I am learning machine learning using Python and understand that I cannot run categorical data through the model, and must first get dummies.  Some of my categorical data has nulls (a very small fraction of only 2 features).  When I convert to dummies, then see if I have missing values it always shows none.  Should I impute beforehand?  Or do I impute categorical data at all?  For instance if the category was male/female, I wouldn't want to replace nulls with the most_frequent.  I see how this would make sense if the feature was income, and I was going to impute missing values.  Income is income, whereas a male is not a female. </p>
  
  <p>So does it make sense to impute categorical data?  Am I way off?  I am sorry this is more applied theory than actual Python programming but was not sure where to post this type of question.</p>