Well, masked arrays are a very good solution for the right problem (i.e. temporarily or permanently flagging data as "bad" while preserving the original data).

Well, masked arrays are a very good solution for the right problem (i.e. temporarily or permanently flagging data as "bad" while preserving the original data). Not to rehash the old debate, but they're quite handy when you need them. I do agree that NaN's are a better choice for truly missing data, but I'm biased just because they use less memory. They're not a solution for non-floating point data, though. Great job on Pandas, by the way!