并行迭代 pandas df答案

【问题标题】：iteration over a pandas df in parallel并行迭代 pandas df
【发布时间】：2019-05-02 20:39:17
【问题描述】：

所以，我想并行迭代 pandas df 所以假设我有 15 行然后我想并行迭代它而不是一个一个地迭代。

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，我正在遍历 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。问题是我在迭代它时一个一个地做。我想并行迭代所有行。

到目前为止，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，这很痛苦。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 
    """

到目前为止，如果我在 df 中使用 15 行并进行如下并行处理，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：- 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。

【问题讨论】：

标签： python python-3.x python-2.7 dataframe lambda

【解决方案1】：

我将从 Reddit 复制并粘贴我的答案到这里（以防有人偶然发现类似情况）：

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')

您可能不得不在apply 方法中使用axis 参数。

【讨论】：

更新：对 Pandas 数据帧进行多处理的另一种好方法是 modin 项目：github.com/modin-project/modin
在最后一行中，lambda x: your_function(x) 可以简化为 your_function

【解决方案2】：

使用线程并使用参数调用线程函数，而不是启动 15 个进程。 threading.Thread(target=func, args=(i,)) 其中 i 是您的号码，func 是包装整个代码的函数。然后遍历它。您不需要在 15 个项目上并行化迭代。

【讨论】：