How to use multiprocess.Pool with .apply function? - python-multiprocessing

Question

I have a function that processes a string and I am applying it on a dataframe column
import pandas as pd
import numpy as np
def test_upper(d):
return d.upper()
def mainfunc():
df = pd.read_csv("file.csv", sep='\t', encoding='utf-8')
print(df.head())
lambdafunc = lambda x: test_upper(x)
df['upper_cols'] = df['cols'].apply(lambdafunc)
print(df.head())
mainfunc()
Now, I want to do the same but with multiprocessing.Pool. I have searched how to do this here in stackoverflow and this is what I came up:
import pandas as pd
import numpy as np
import multiprocessing as mp
def test_upper(d):
return d.upper()
def mainfunc():
df = pd.read_csv("file.csv", sep='\t', encoding='utf-8')
print(df.head())
lambdafunc = lambda x: test_upper(x)
list_results = pd.Series()
def log_result(result):
list_results.append(result)
pool = mp.Pool(processes=4)
pool.apply_async(lambdafunc, (df['cols'], ), callback=log_result)
pool.close()
pool.join()
print(list_results)
mainfunc()
The results are blank Series/list as I've tried both. What am I doing wrong here?
Thank you!

Answer 1

Finally figured it out
def test_upper(d):
output = d.apply(lambda x: x:upper())
return output
def mainfunc():
df = pd.read_csv("file.csv", sep='\t', encoding='utf-8')
print(df.head())
pool = mp.Pool(processes=4)
result = pool.apply_async(test_upper, (df['cols'], ))
pool.close()
pool.join()
print(result.get())
mainfunc()