Parallel computing in Python Similar to MATLAB

Question

I have been using parfor in MATLAB to run parallel for loops for quite some time. I need to do something similar in Python but I cannot find any simple solution. This is my code:

t = list(range(1,3,1))
G = list(range(0,3,2))
results = pandas.DataFrame(columns = ['tau', 'p_value','G','t_i'],index=range(0,len(G)*len(t)))
counter = 0  
for iteration_G in list(range(0,len(G))):
    for iteration_t in list(range(0,len(t))):
        
        matrix_1,matrix_2 = bunch of code


        tau, p_value = scipy.stats.kendalltau(matrix_1, matrix_2)
        results['tau'][counter] = tau
        results['p_value'][counter] = p_value
        results['G'][counter] = G[iteration_G]
        results['t_i'][counter] = G[iteration_t]
        counter = counter + 1

I would like to use the parfor equivalent in the first loop.

Answer 1

I found a solution using parfor. It is still a bit more complicated than MATLAB's parfor but it's pretty close to what I am used to.

t = list(range(1,16,1))
G = list(range(0,62,2))
for iteration_t in list(range(0,len(t))):
    @parfor(list(range(0,len(G))))
    def fun(iteration_G):
        result = pandas.DataFrame(columns = ['tau', 'p_value'],index=range(0,1))

        matrix_1,matrix_2 = bunch of code   



        tau, p_value = scipy.stats.kendalltau(matrix_1, matrix_2)     
        result['tau'] = tau
        result['p_value'] = p_value
        fun = numpy.array([tau,p_value])
        return fun