Question

我希望基于“ col1”列的值将数据帧拆分为多个数据帧，并使用多重处理将拆分后的数据帧分配给每个核心。

数据框：

   col  col1
0   0   a
1   1   a
2   2   b
3   3   a
4   4   c
5   5   c
6   6   a
7   7   c
8   8   b
9   9   a

import multiprocessing
import pandas as pd
import numpy as np
from multiprocessing import Pool, cpu_count

cores = cpu_count() 
partitions = cores

df = pd.DataFrame({'col': [0,1,2,3,4,5,6,7,8,9],
              'col1':['a','a','b','a','c','c','a','c','b','a']})

def parallelize_dataframe(df, func):
    data = np.array_split(df, partitions)
    print(data)
    pool = Pool(cores)
    df = pd.concat(pool.map(func, data))
    pool.close()
    pool.join()
    return df


def square(x):
    return x**2

def test_func(data):
    data["square"] = data["col"].apply(square)
    return data

test = parallelize_dataframe(df, test_func)

预期的数据帧分割

和

    col col1
2   2   b
8   8   b

类似于“ col1”列中的唯一值

然后使用多重处理将分割后的数据帧分配给每个核心，并对其应用功能。

请帮助我拆分数据帧，并将其分配给每个内核进行并行处理。

Answer 1

import math
import multiprocessing 
import pandas as pd


df = pd.DataFrame({'col': [0,1,2,3,4,5,6,7,8,9],'col1':['a','a','b','a','c','c','a','c','b','a']})

num_split_df = math.floor(len(df)/2) # 2 - splits in df

m = multiprocessing.Manager()
q = m.Queue() # use this manager Queue instead of multiprocessing Queue as that causes error

pool_tuple = [(i,q,df_emp[(i * 6):((i + 1) * 6)]) for i in range(num_split)] # 6 - rows in each df

with multiprocessing.Pool(processes=4) as pool: # number of cores
    results = pool.starmap(multiprocessing_func, pool_tuple)

def multiprocessing_func(num, q, df):
    ...

熊猫数据帧分割和多处理

1 个答案: