It might be annoying, but it's certainly not bizarre. In the floating point standard (IEEE 754) there are reserved bit values (with hardware support) to represe

It might be annoying, but it's certainly not bizarre. In the floating point standard (IEEE 754) there are reserved bit values (with hardware support) to represent NaNs. For integers no such thing exists, so you're left with a bunch of different implementation choices all with different tradeoffs. A long time ago NumPy devs choose not to support NaN in integer arrays at all (for maximum performance), and Pandas (starting as a wrapper around numpy arrays) inherited that. For a more technical discussion of the performance implications of one possible implementation, see http://wesmckinney.com/blog/bitmaps-vs-sentinel-values/ http://wesmckinney.com/blog/bitmaps-vs-sentinel-values/