如何构建多个源的并行执行时间的表/tibble/df？

import pandas as pd
from multiprocessing import Pool

# 模拟数据源
data_source1 = [1, 2, 3, 4, 5]
data_source2 = [6, 7, 8, 9, 10]

# 定义计算函数
def calculate_sum(data):
    return sum(data)

if __name__ == '__main__':
    # 创建进程池
    with Pool(processes=2) as pool:
        # 并行计算
        results = pool.map(calculate_sum, [data_source1, data_source2])
    
    # 构建DataFrame
    df = pd.DataFrame({
        'Source': ['Source1', 'Source2'],
        'Sum': results
    })
    
    print(df)

library(dplyr)
library(parallel)

# 模拟数据源
data_source1 <- c(1, 2, 3, 4, 5)
data_source2 <- c(6, 7, 8, 9, 10)

# 定义计算函数
calculate_sum <- function(data) {
    return(sum(data))
}

# 并行计算
num_cores <- detectCores() - 1
cl <- makeCluster(num_cores)
results <- parLapply(cl, list(data_source1, data_source2), calculate_sum)
stopCluster(cl)

# 构建tibble
df <- tibble(Source = c('Source1', 'Source2'), Sum = results)

print(df)