pandas multiprocessing apply

Question

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 d

Answer 1

To use all (physical or logical) cores, you could try mapply as an alternative to swifter and pandarallel.

You can set the amount of cores (and the chunking behaviour) upon init:

import pandas as pd
import mapply

mapply.init(n_workers=-1)

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

def process(df):
    # spawns a pathos.multiprocessing.ProcessPool if sensible
    res = df.mapply(process_apply, axis=1)
    return res

By default (n_workers=-1), the package uses all physical CPUs available on the system. If your system uses hyper-threading (usually twice the amount of physical CPUs would show up), mapply will spawn one extra worker to prioritise the multiprocessing pool over other processes on the system.

You could also use all logical cores instead (beware that like this the CPU-bound processes will be fighting for physical CPUs, which might slow down your operation):

import multiprocessing
n_workers = multiprocessing.cpu_count()

# or more explicit
import psutil
n_workers = psutil.cpu_count(logical=True)