I have scipy and numpy, Python v3.1

I need to create a 1D array of length 3million, using random numbers between (and including) 100-60,000. It has to fit a normal distribution.

Using 'a = numpy.random.standard_normal(3000000)', I get a normal distribution for that required length; not sure how to achieve the required range.

A standard normal distribution has mean 0 and standard deviation 1. What I understand from your requirements is that you need a ((60000-100)/2, (60000-100)/2) one. Take each value from standard_normal() result, multiply it by the new variance, and add the new mean.

I haven't used NumPy, but a quick search of the docs says that you can achieve what you want directly bu using numpy.random.normal()

One last tidbit: normal distributions are not bounded. That means there isn't a value with probability zero. Your requirements should be in terms of means and variances (or standard deviations), and not of limits.