熊猫多处理适用

Question

我正在尝试将多处理与pandas数据帧一起使用,即将数据帧拆分为8个部分.使用apply(每个部分在不同的过程中处理)对每个部分应用一些功能.

编辑:这是我最终找到的解决方案:

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'])

Answer 1

基于作者解决方案的更通用的版本，允许在每个函数和数据帧上运行它：

from multiprocessing import  Pool
from functools import partial
import numpy as np

def parallelize(data, func, num_of_processes=8):
    data_split = np.array_split(data, num_of_processes)
    pool = Pool(num_of_processes)
    data = pd.concat(pool.map(func, data_split))
    pool.close()
    pool.join()
    return data

def run_on_subset(func, data_subset):
    return data_subset.apply(func, axis=1)

def parallelize_on_rows(data, func, num_of_processes=8):
    return parallelize(data, partial(run_on_subset, func), num_of_processes)

所以下面这行：

df.apply(some_func, axis=1)

会变成：

parallelize_on_rows(df, some_func) 

Answer 2

这是我发现有用的一些代码。自动将数据帧分割成您拥有的任意多个 CPU 核心。

import pandas as pd
import numpy as np
import multiprocessing as mp

def parallelize_dataframe(df, func):
    num_processes = mp.cpu_count()
    df_split = np.array_split(df, num_processes)
    with mp.Pool(num_processes) as p:
        df = pd.concat(p.map(func, df_split))
    return df

def parallelize_function(df):
    df[column_output] = df[column_input].apply(example_function)
    return df

def example_function(x):
    x = x*2
    return x

跑步：

df_output = parallelize_dataframe(df, parallelize_function)

Answer 3

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)

Answer 4

这对我来说效果很好：

rows_iter = (row for _, row in df.iterrows())

with multiprocessing.Pool() as pool:
    df['new_column'] = pool.map(process_apply, rows_iter)

Answer 5

由于我没有太多的数据脚本，这是一个猜测，但我建议使用p.map而不是apply_async回调。

p = mp.Pool(8)
pool_results = p.map(process, np.array_split(big_df,8))
p.close()
p.join()
results = []
for result in pool_results:
    results.extend(result)