multiprocess module vs multiprocessing module

问题

I have a function which I want to map to a list in parallel. I achieved the task by using multiprocess module. However, when I want to run this task with multiprocessing module, it seems that the processes has spawn but none of them start working.

The function and configuration of multiprocess module is as follow:

import pandas as pd
my_file = pd.read_csv('my_file.txt')

from datetime import date, timedelta
start_dates = []
for i in range(17):
    today = date.today() - timedelta(days = i)
    start_dates.append(today)
start_dates = pd.DataFrame(start_dates, columns = ['Date'])
start_dates = start_dates.merge(my_file)
start_dates = start_dates.values.tolist()

from multiprocess import Pool
pool = Pool(processes = 8)

my_func(valu):
    import pandas as pd
    df = read_csv('df.txt')
    df = df[df.Date > vale]
    #lots of other computations
    return final_df

result = pool.map(my_func, [st for st in start_dates])
pool.close()

results = pd.concat(result)

And the function and configuration of the multiprocessing module is as below:

my_func(valu):
    import pandas as pd
    df = read_csv('df.txt')
    df = df[df.Date > vale]
    #lots of other computations
    return final_df

from multiprocessing import Pool

import pandas as pd
my_file = pd.read_csv('my_file.txt')

from datetime import date, timedelta
start_dates = []
for i in range(17):
    today = date.today() - timedelta(days = i)
    start_dates.append(today)
start_dates = pd.DataFrame(start_dates, columns = ['Date'])
start_dates = start_dates.merge(my_file)
start_dates = start_dates.values.tolist()

def mp():
    pool = Pool(processes = 8)
    result = pool.map(my_func, [st for st in start_dates])
    return result

if __name__ == '__main__':
    mp()

results = pd.concat(result)

Is the problem relates to the order of the multiprocessing configuration which I wrote?

Any help is appreciated.

来源：https://stackoverflow.com/questions/61564223/multiprocess-module-vs-multiprocessing-module