Tooling is probably the least exciting topic in data science at the moment. People seem to be more interested in speaking about the latest chatbot technology or deep learning framework. This just does not make sense. Why would you not dedicate enough time to pick your tools carefully?
Missing data are probably the most widespread source of errors in your code, and the reason for most of the exception-handling. If you try to remove them, you might reduce the amount of data you have available dramatically — probably the worst that can happen in machine learning.