当您并行化代码时,如何保存两个独立的CSV?

import os
import multiprocessing
import pandas as pd


def single_iteration(iter: int):
    some_parameters = 100

    data1 = [1] * 100
    data2 = [2] * 100
    data3 = [3] * 100
    data4 = [4] * 100

    result1 = []
    result2 = []

    for i in range(100):
        data1_dict = {'Col1': data1[i], 'Col2': data2[i]}
        data2_dict = {'Col3': data3[i], 'Col4': data4[i]}
        result1.append(data1_dict)
        result2.append(data2_dict)

    df1 = pd.DataFrame(result1)
    df2 = pd.DataFrame(result2)
    return df1, df2


if __name__ == "__main__":
    run_stop = 100
    number_of_cores = int(os.environ.get('SLURM_CPUS_PER_TASK', 4))

    with multiprocessing.Pool(number_of_cores) as pool:
        results = pool.map(single_iteration, range(run_stop))

    # Separate the results into two lists of DataFrames
    df1_list = [res[0] for res in results]
    df2_list = [res[1] for res in results]

    # Concatenate all DataFrames in each list
    df1 = pd.concat(df1_list, ignore_index=True)
    df2 = pd.concat(df2_list, ignore_index=True)

    df1.to_csv('df1_file_name.csv', index=False)
    df2.to_csv('df2_file_name.csv', index=False)