Numpy integer nan [duplicate]
No, you can't, at least with current version of NumPy. A nan is a special value for float arrays only.
There are talks about introducing a special bit that would allow non-float arrays to store what in practice would correspond to a nan, but so far (2012/10), it's only talks.
In the meantime, you may want to consider the numpy.ma package: instead of picking an invalid integer like -99999, you could use the special numpy.ma.masked value to represent an invalid value.
a = np.ma.array([1,2,3,4,5], dtype=int)a[1] = np.ma.maskedmasked_array(data = [1 -- 3 4 5], mask = [False True False False False], fill_value = 999999)