Question

所以，我想并行地遍历pandas df，所以假设我有15行，那么我想并行而不是一个接一个地遍历它。

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' }

])

因此，我遍历df并制作了命令行，然后将输出存储在df中并进行了数据过滤，最后将其存储到influxdb中。问题是我在迭代过程中一个接一个地做。我想在所有行上进行并行迭代。

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。当我必须在所有20个脚本中进行更改时，这是一种痛苦。我的脚本如下所示：-

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中拥有15行并进行如下所示的并行处理，则我将拥有15个脚本：-

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：-如何迭代df，并使所有15行并行运行，并完成for循环中的所有工作。

Answer 1

我将在Reddit上复制并粘贴我的答案（以防有人偶然遇到类似情况）

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')

您可能必须在apply方法中使用axis参数。

Answer 2

与其启动15个进程，不如使用线程并使用参数调用线程函数。 threading.Thread(target=func, args=(i,))，其中i是您的电话号码，func是包装整个代码的函数。然后遍历它。您不需要并行化15个项目。

在熊猫df上并行迭代

2 个答案: