You can do parallel computations in Python e.g., many numpy functions can utilize more than one CPU:

  import numpy as np

  N = 5000
  # create a couple NxN matrices with random elements
  a = np.random.rand(N, N) 
  b = np.random.rand(N, N)
  # perform matrix multiplication
  c = np.dot(a, b)

Cython makes it easy to write C extensions for Python if necessary.

You can also do a compute-intensive work in parallel using multiple processes:

  import random
  from multiprocessing import Pool
  
  def fire(nshots, rand=random.random):
      return sum(1 for _ in range(nshots) if (rand()**2 + rand()**2) < 1)
  
  def main():
      pool = Pool() # use all available CPUs
      nshots, nslices = 10**6, 10
      nhits = sum(pool.imap(fire, [nshots // nslices] * nslices))
      print("pi = {pi:.5}".format(pi=4.0 * nhits / nshots))
  
  if __name__ == '__main__':
      main()

The code calculates Pi using Monte Carlo method http://demonstrations.wolfram.com/MonteCarloEstimateForPi/