title: How to sample M times from N different normal distributions in python? Is there a "faster" way in terms of processing time?
body: <p>I need to sample multiple (M) times from N different normal distributions. This repeated sampling will happen in turn several thousand times. I want to do this in the most efficient way, because I would like to not die of old age before this process ends. The code would look something like this:</p>
  <pre><code>import numpy as np
  
  # bunch of stuff that is unrelated to the problem
  
  number_of_repeated_processes = 5000
  number_of_samples_per_process = 20
  
  # the normal distributions I'm sampling from are described by 2 vectors:
  #
  # myMEANS &lt;- an numpy array of length 10 containing the means for the distributions
  # myVAR &lt;- an numpy array of length 10 containing the variance for the distributions
  
  
  for i in range(number_of_repeated_processes):
    # myRESULT is a list of arrays containing the results for the sampling
    #
    myRESULT = [np.random.normal(loc=myMEANS[j], scale=myVAR[j], size = number_of_samples_per_process) for j in range(10)]
    #
    # here do something with myRESULT
  
  # end for loop
  </code></pre>
  <p>The questions is... is there a better way to obtain the myRESULT matrix</p>