Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
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.