pandas multiprocessing apply

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 different process).

EDIT: Here's the solution I finally found:

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'])

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)

A more generic version based on the author solution, that allows to run it on every function and dataframe:

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)

So the following line:

df.apply(some_func, axis=1)

Will become:

parallelize_on_rows(df, some_func) 

This is some code that I found useful. Automatically splits the dataframe into however many cpu cores you have.

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

To run:

df_output = parallelize_dataframe(df, parallelize_function)

Since I don't have much of your data script, this is a guess, but I'd suggest using p.map instead of apply_async with the callback.

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)

This worked well for me:

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

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

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)

I also run into the same problem when I use multiprocessing.map() to apply function to different chunk of a large dataframe.

I just want to add several points just in case other people run into the same problem as I do.

1. remember to add if __name__ == '__main__':
2. execute the file in a .py file, if you use ipython/jupyter notebook, then you can not run multiprocessing (this is true for my case, though I have no clue)

Install Pyxtension that simplifies using parallel map and use like this:

from pyxtension.streams import stream

big_df = pd.concat(stream(np.array_split(df, multiprocessing.cpu_count())).mpmap(process))

I ended up using concurrent.futures.ProcessPoolExecutor.map in place of multiprocessing.Pool.map which took 316 microseconds for some code that took 12 seconds in serial.

Python's pool.starmap() method can be used to succinctly introduce parallelism also to apply use cases where column values are passed as arguments, i.e. to cases like:

df.apply(lambda row: my_func(row["col_1"], row["col_2"], ...), axis=1)

Full example and benchmarking:

import time
from multiprocessing import Pool

import numpy as np
import pandas as pd


def mul(a, b, c):
    # For illustration, could obviously be vectorized
    return a * b * c

df = pd.DataFrame(np.random.randint(0, 100, size=(10_000_000, 3)), columns=list('ABC'))

# Standard apply
start = time.time()
df["mul"] = df.apply(lambda row: mul(row["A"], row["B"], row["C"]), axis=1)
print(f"Standard apply took {time.time() - start:.0f} seconds.") 

# Starmap apply
start = time.time()
with Pool(10) as pool:
    df["mul_pool"] = pool.starmap(mul, zip(df["A"], df["B"], df["C"]))
print(f"Starmap apply took {time.time() - start:.0f} seconds.") 

pd.testing.assert_series_equal(df["mul"], df["mul_pool"], check_names=False)


>>> Standard apply took 72 seconds.
>>> Starmap apply took 5 seconds.

This has the benefit of not relying on external libraries, plus being very readable.

Tom Raz's answer https://stackoverflow.com/a/53135031/11847090 misses an edge case where there are fewer rows in the dataframe than processes

use this parallelize method instead

def parallelize(data, func, num_of_processes=8):
   # check if the number of rows is less than the number of processes
   # to avoid the following error
   # ValueError: Expected a 1D array, got an array with shape
   num_rows = len(data)
    if num_rows == 0:
        return None
    elif num_rows < num_of_processes:
        num_of_processes = num_rows
    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

and also I used dask bag to multithread this instead of this custom code