import multiprocessing
import pandas as pd
import numpy as np
from multiprocessing import Pool
num_partitions = 5
num_cores = multiprocessing.cpu_count()

def parallelize_dataframe(df, func):
    a,b,c,d,e = np.array_split(df, num_partitions)
    pool = Pool(num_cores)
    df = pd.concat(pool.map(func, [a,b,c,d,e]))
    pool.close()
    pool.join()
    return df

def square(x):
    return x**2

def test_func(data):
    print("Process working on: ",data)
    data["square"] = data["col"].apply(square)
    return data

df = pd.DataFrame({'col': [0,1,2,3,4,5,6,7,8,9]})

if __name__ == '__main__':
    test = parallelize_dataframe(df, test_func)
    print(test)