pandas_apply_parallel.py

import multiprocessing
from multiprocessing import Pool

import pandas as pd
import numpy as np

def apply_parallel(df, func):    
    cores = multiprocessing.cpu_count()
    partitions = np.array_split(df, cores)
    with Pool(cores) as pool:
        df = pd.concat(pool.map(func, partitions))
    return df

def func_apply_row(row):
  return "whatever"

def apply_row(df):
    return df.apply(func_apply_row, axis=1)
  
df['some_col'] = apply_parallel(df, apply_row)