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I would like to speed up pandas apply function. I have been using swifter . It currently takes about 5 mins for 200000 records using multiprocessing as below . Is there any way to speed this up further .

def partial_match(source_words, dest_words):
    matched_words = ''
    if any(word in dest_words for word in source_words) :
        match_words_list = set(source_words)&set(dest_words) 
        matched_words = ",".join(match_words_list)
    return matched_words

def exact_match(source_words, dest_words):
    matched_words = ''
    if all(word in dest_words for word in source_words) :
        match_words_list = set(source_words)&set(dest_words) 
        matched_words = ",".join(match_words_list)
    return matched_words


series_index = ['match_type', 'matched_words'   ]
def perform_match(x):
    match_series = pd.Series(np.repeat('', len(series_index)), index = series_index)
    if x['remove_bus_ending'] == 'Y':
        x['dest_words'] = x['dest_words_2']
    else:
        x['dest_words'] = x['dest_words_1']
    # exact match
    if (x['partial_match_flag'] == 'Y') :
        match_series['matched_words'] = partial_match(x['source_words'], x['dest_words'])
        if match_series['matched_words'] != '':
            match_series['match_type'] = 'Partial Match'
    elif (x['exact_match_2'] == 'Y'):
        match_series['matched_words'] = exact_match(x['source_words'], x['dest_words'])
        if match_series['matched_words'] != '':
            match_series['match_type'] = 'Exact Match' 

    return match_series

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.swifter.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)

df[match_series]  = parallelize_on_rows(df, perform_match)

below is some sample data

flag1   partial_match_flag  exact_match_flag    source_words    dest_word_2 dest_words_1
0   N   Y   N   [song, la]  [urban, karamay, credit, city, co, kunlun, com...   [ltd, urban, karamay, credit, city, co, kunlun...
1   N   Y   N   [song, la]  [al, abdulah, nasser]   [al, abdulah, nasser]
2   N   Y   N   [song, la]  [al, abdulah, nasser]   [al, abdulah, nasser]
3   N   Y   N   [song, la]  [abdulamir, mahdi]  [abdulamir, mahdi]
4   N   Y   N   [song, la]  [abdullah, al, nasser]  [abdullah, al, nasser]
5   N   Y   N   [song, la]  [abu, al, jud]  [abu, al, jud]
6   N   Y   N   [song, la]  [al, herz, adam]    [al, herz, adam]
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flag as boolean

If you change the flags from 'Y' and 'N' to True and False You can use boolean indexing. This should speed up a lot of things already

set

You check for each combination word in dest_words for word in source_words on a list of words. If the check matches, you convert to a set. The containment check would be sped up by checking against a list, but using set comparisons would speed this up a lot.

import typing

def partial_match(
    source_words: typing.Set[str], dest_words: typing.Set[str], index=None
) -> typing.Tuple[typing.Any, typing.Optional[str]]:
    intersection = source_words & dest_words
    if intersection:
        return index, ", ".join(intersection)
    return index, None

def exact_match(
    source_words: typing.Set[str], dest_words: typing.Set[str], index=None
) -> typing.Tuple[typing.Any, typing.Optional[str]]:
    if source_words == dest_words:
        return index, ", ".join(source_words)
    return index, None

The reason I chose to return the index along with it is to be able to reconstruct the series easier when reassembling everything.

Don't touch the original data

You change your source data inplace (by adding columns). Better would be to leave this untouched, and keep the destination words etc in separate series.

Series.where

You can replace calls like this

if x['remove_bus_ending'] == 'Y':
        x['dest_words'] = x['dest_words_2']
    else:
        x['dest_words'] = x['dest_words_1']

with Series.where

a = pd.Series(list("abcd"))
b = pd.Series(list("efgh"))
c = pd.Series([True, True, False, True])
b.where(c, other=a)
0    e
1    f
2    c
3    h
dtype: object

If your data looks like this:

from io import StringIO

import pandas as pd

def setify(s):
    return s.str.strip("[]").str.split(", ").apply(set)

df = pd.read_csv(StringIO(data_str), sep="\s\s+", index_col=False, engine='python')
df["source_words"] = setify(df["source_words"])
df["dest_words_1"] = setify(df["dest_words_1"])
df["dest_word_2"] = setify(df["dest_word_2"])
df["remove_bus_ending"] = df["remove_bus_ending"] == "Y"
df["partial_match_flag"] = df["partial_match_flag"] == "Y"
df["exact_match_flag"] = df["exact_match_flag"] == "Y"

intermediate dataframe

If you want to split the dataframe with arraysplit, you'll need to provide an intermediate form with the info you need:

df_intermediate = pd.concat(
    [
        df["dest_word_2"]
        .where(df["remove_bus_ending"], other=df["dest_words_1"])
        .rename("dest_words"),
        df["source_words"],
    ],
    axis=1,
)

You can even split it immediately according to what matching is needed

df_intermediate_partial = df_intermediate.loc[df["partial_match_flag"]]
df_intermediate_exact = df_intermediate.loc[df["exact_match_flag"]]

applying the function

not parallel:

result_partial = list(
    map(
        partial_match,
        df_intermediate_partial["source_words"],
        df_intermediate_partial["dest_words"],
        df_intermediate_partial.index,
    )
)


results_exact = list(
    map(
        exact_match,
        df_intermediate_exact["source_words"],
        df_intermediate_exact["dest_words"],
        df_intermediate_exact.index,
    )
)

result = pd.Series(result_partial + results_exact)

This should be easy to parallelize. Since I'm no expert on that, I'll leave that to others.

context manager

Most of the examples I found in the multiprocessing documantation work with a context manager that takes care of the closing of the pool

with Pool(processes=4) as pool:
    ... # parallel part of the code
| improve this answer | |
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  • \$\begingroup\$ flagging as boolean and setifying data has improved speed drastically \$\endgroup\$ – abhilash Dasari Jun 11 at 6:08

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