并行迭代pandas df [英] iteration over a pandas df in parallel

问题描述

所以，我想并行迭代一个 Pandas df，所以假设我有 15 行，那么我想并行迭代它而不是一一迭代.

So, I want to iterate over a pandas df in parallel so suppose i am having 15 rows then i want to iterate over it parallel and not one by one.

df:-

df = pd.DataFrame.from_records([
    {'domain':'dnd','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'hrpd','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'blhp','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'rbswp','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'foxbp','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'rbsxbp','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'dnd','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' },
    {'domain':'hrpd','duration':'90','media_file':'testfont.wav','user':'tester_food','channel':'confctl-2' }
   
])

所以，我正在迭代 df 并制作命令行，然后将输出存储在 df 中并进行数据过滤，最后将其存储到 influxdb 中.问题是我正在一个一个地做，因为我正在迭代它.我想并行迭代所有行.

So, I am iterating over the df and making command line and then storing the output in a df and doing data filtering and then finally storing it into influxdb. The problem is i am doing it one by one as i am iterating over it. what i want to iterate over all the rows in parallel.

截至目前，我已经制作了 20 个脚本并使用多处理并行处理所有脚本.当我必须进行更改时很痛苦，因为我必须在所有 20 个脚本中进行更改.我的脚本如下所示:-

As of now i have made 20 scripts and using multiprocessing to go over all the scripts in parallel. Its a pain when i have to do a change as i have to do it in all 20 scripts. My script looks like below :-

for index, row in dff.iterrows():
    domain = row['domain']
    duration = str(row['duration'])
    media_file = row['media_file']
    user = row['user']
    channel = row['channel']
    cmda = './vaa -s https://' + domain + '.www.vivox.com/api2/ -d ' + 
    duration + ' -f ' + media_file + ' -u .' + user + '. -c 
    sip:confctl-2@' + domain + '.localhost.com -ati 0ps-host -atk 0ps- 
    test'

    rows = [shlex.split(line) for line in os.popen(
    cmda).read().splitlines() if line.strip()]

    df = pd.DataFrame(rows)
    """
    Bunch of data filteration and pushing it into influx 
    """

截至目前，如果我在 df 中处理 15 行并执行如下并行处理，我将有 15 个脚本:-

As of now i am having 15 script if i am hvaing 15 rows in df and doing parallel processing like below :-

import os
import time
from multiprocessing import Process
os.chdir('/Users/akumar/vivox-sdk-4.9.0002.30719.ebb523a9')
def run_program(cmd):
    # Function that processes will run
    os.system(cmd)

# Creating command to run
commands = ['python testv.py']
commands.extend(['python testv{}.py'.format(i) for i in range(1, 15)])

# Amount of times your programs will run
runs = 1

for run in range(runs):
    # Initiating Processes with desired arguments
    running_programs = []
    for command in commands:
        running_programs.append(Process(target=run_program, args=(command,)))
        running_programs[-1].daemon = True

    # Start our processes simultaneously
    for program in running_programs:
        program.start()

    # Wait untill all programs are done
    while any(program.is_alive() for program in running_programs):
        time.sleep(1)

问题:- 我如何遍历 df 并使所有 15 行并行运行并在 for 循环中执行所有操作.

推荐答案

我将在 Reddit 上复制并粘贴我的回答(以防有人遇到类似情况):

I'm gonna copy and paste my answer from Reddit on here (in case anyone stumbles upon it with a similar situation):

import dask.dataframe as ddf

def your_function(row):
    domain = row['domain']
    duration = str(row['duration'])
    media_file = row['media_file']
    user = row['user']
    channel = row['channel']
    cmda = './vaa -s https://' + domain + '.www.vivox.com/api2/ -d ' + 
    duration + ' -f ' + media_file + ' -u .' + user + '. -c 
        sip:confctl-2@' + domain + '.localhost.com -ati 0ps-host -atk 0ps- test'

    rows = [shlex.split(line) for line in os.popen(
            cmda).read().splitlines() if line.strip()]

df_dask = ddf.from_pandas(df, npartitions=4)   # where the number of partitions is the number of cores you want to use
df_dask['output'] = df_dask.apply(lambda x: your_function(x), meta=('str')).compute(scheduler='multiprocessing')

您可能需要在 apply 方法中使用轴参数.

You might have to play around with the axis parameter in the apply method.

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