I'm trying to analyze voting patterns of Ukraine's parliament deputies. I scraped all the data on their voting during last session. Each data entry has following information: Deputy name, date, bill number, vote. The vote field can be yes, no, didn't vote (treated as no) and not present. My problem is with "not present" vote type. I don't want to have deputies that were often absent to be classified as similar as I only care when they voted yes or no. I encoded votes as dummy variables, but not sure what to do with "not present" vote. Treat it as No will definitely skew the results making those who missed a lot of voting sessions look alike. Removing whole columns where one of the deputies wasn't present is also not a solution as I will have no data remaining.
My primary tool of analysis is python with scikit-learn.