Parallel creation of complex dataframes

Question

The below code seems to have some issues. The aim would be to append each result of new_df() to some list, e.g. out.

import pandas as pd
import random
import time
from multiprocessing import Pool

def new_df(rows=10000):  # proxy for complex dataframe
    temp = pd.DataFrame({'a': [''.join(chr(random.randint(65,122)) for _ in range(200))
                               for _ in range(rows)]})
    temp['b'] = temp['a'].str.lower()
    temp['c'] = temp['a'].str.upper()
    return temp

pool = Pool(4)
start = time.time()
out = pool.map(new_df, [9999,10000,10001,10002])
print(f"{time.time() - now} sec")

Issues - VisualStudioCode

    raise RuntimeError('''
RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

Answer 1

Code reconstructed to utilise the main module idiom:

import pandas as pd
import random
import time
from multiprocessing import Pool

def new_df(rows=10000):
    temp = pd.DataFrame({'a': [''.join(chr(random.randint(65,122)) for _ in range(200))
                               for _ in range(rows)]})
    temp['b'] = temp['a'].str.lower()
    temp['c'] = temp['a'].str.upper()
    return temp

def main():
    start = time.perf_counter()
    with Pool(4) as pool:
        pool.map(new_df, [9999, 10000, 10001, 10002])
    print(f"{time.perf_counter() - start:.2f}s")

if __name__ == '__main__':
    main()

Output:

1.24s