This shows how to first generate such arrays (in steps, as every
transaction change should fit in memory), and then gather data from
whole array using C/Fortran/etc code.

It shows how to compute mean via NumPy's ndarray.mean()

It also shows that e.g. ndarray.var() wants to create temporaries in
size of original ndarray and that would fail, because it does not fit
into RAM.

ndarray.var() should not need to create such temporaries in principle -
all it has to do is to first compute mean, and then compute

    sum (Xi - <X>)^2

in a loop.

<X> is scalar, Xi - is just access to original array.

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So this also show NumPy can be incrementally improved to avoid creating
such temporaries, and then it will work.