pandas 多处理应用

pandas multiprocessing apply( pandas 多处理应用)

问题描述

我正在尝试对 pandas 数据帧使用多处理，即将数据帧拆分为 8 个部分.使用 apply 对每个部分应用一些功能(每个部分在不同的过程中处理).

I'm trying to use multiprocessing with pandas dataframe, that is split the dataframe to 8 parts. apply some function to each part using apply (with each part processed in different process).

这是我终于找到的解决方案:

Here's the solution I finally found:

import multiprocessing as mp
import pandas.util.testing as pdt

def process_apply(x):
    # do some stuff to data here

def process(df):
    res = df.apply(process_apply, axis=1)
    return res

if __name__ == '__main__':
    p = mp.Pool(processes=8)
    split_dfs = np.array_split(big_df,8)
    pool_results = p.map(aoi_proc, split_dfs)
    p.close()
    p.join()

    # merging parts processed by different processes
    parts = pd.concat(pool_results, axis=0)

    # merging newly calculated parts to big_df
    big_df = pd.concat([big_df, parts], axis=1)

    # checking if the dfs were merged correctly
    pdt.assert_series_equal(parts['id'], big_df['id'])

推荐答案

你可以使用 https://github.com/nalepae/pandarallel，如下例所示:

You can use https://github.com/nalepae/pandarallel, as in the following example:

from pandarallel import pandarallel
from math import sin

pandarallel.initialize()

def func(x):
    return sin(x**2)

df.parallel_apply(func, axis=1)

这篇关于 pandas 多处理应用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！