Implementing for loops as batches

I’m performing 2 big for loop tasks on a dataframe column. The context being what I’m calling “text corruption”; turning perfectly structured text into text full of both missing punctuation and misspellings, to mimic human errors.

I found that running 10,000s rows was extremely slow, even after optimizing the for loops.

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I discovered a process called Batching, on this post.

The top answer provides a concise template that I imagine is much faster than regular for loop iterations.

How might I use that answer to reimplement the following code? (I added a comment to it asking more about it).

Or; might there be any technique that makes my for loops considerably quicker?

import pandas as pd
import random
import re

# example
df = pd.DataFrame(columns=['Forname', 'Surname', 'Sentence'])
df.loc['0'] = ['Bob', 'Smith', 'Hi, this is a perfectly constructred sentence!']
df.loc['1'] = ['Alice', 'Smith', 'Can you tell this is fake data?']
df.loc['2'] = ['John', 'Smith', 'This poster needs help!']
df.loc['3'] = ['Michael', 'Smith', 'Apparently, this poster is sturggling a bit LOL']
df.loc['4'] = ['Daniel', 'Smith', 'More fake data here; ok.']
df.loc['5'] = ['Sarah', 'Smith', 'Will need to think up of better ideas.']
df.loc['6'] = ['Matthew', 'Smith', 'Love a good bit of Python, me.']
df.loc['7'] = ['Jane', 'Smith', 'Is this a sentence?! (I think so).']
df.loc['8'] = ['Peter', 'Smith', "Remarkable - isn't it?"]
df.loc['9'] = ['Chloe', 'Smith', "Foo Bar... that's all that is left to say."]

print(df)

punctuation_marks = ['?', '…', '!', '.', ',', '—', '–', '–', ':', ';', '"', ''', '[', ']', '(', ')', '{', '}']
p = 0.5 # changeable

for idx, string in enumerate(df['Sentence']):
  for punc in punctuation_marks:
    if punc in string:
      CHANCE = (random.randint(1, 100)) / 100
      if CHANCE <= p:
        df['Sentence'][idx] = string.replace(punc, '')

misspellings_corpus = open('misspellings_corpus.txt', 'r')
misspellings = misspellings_corpus.readlines()

for idx, string in enumerate(df['Sentence']):
  word_list = re.sub("[^w]", " ",  string).split() # removes punctuation
  for word in word_list:
    CHANCE = (random.randint(1, 100)) / 100
    try: # break middle for-loop
      for ms in misspellings:
        if (word in ms) and (CHANCE <= p):
          wrong = ms.split('->')[0]
          correct = ms.split('->')[1][:-2] # removes 'n'
          if ',' in correct: correct = random.choice(my_str.split(',')).strip() # only 1 correct spelling
          if correct in string:
            df['Sentence'][idx] = string.replace(correct, wrong)
            raise StopIteration
    except StopIteration: pass

misspellings_corpus.txt (snippet):

affadvit,affa_dava,afadant,afadavate,afadavid,affidate,affidavent,afftadave,athadavid,affiadait,aphadivode,appidavid,afidaded,affi-davit,affidavat,aphadated,affivadat,afidaviat,affedavit,affiavate,affidaved,afefedavid,affidavate,affavidate,affdated,aphidavit,affevivat,affided,affadavid,attipdavid,affidavidit,affidavite,affadivate,affidavited,afdiodave,affidafet,affidivit,afadafit,affedit,afadavide,afidefed,Affi_David,affividate,affaidivit,afidiated,affidovt,affadavat,avadavate,effidavit,afidavit,aphadavid,afedaved,afardivient,apitated,affividative,affedaivite,afteradeated,Afi_David,acavated,affedated,affidevit,affidivat,afaedaviate,affedaved,afatait,afedative,avidated,afidavid,avidiate,afadavit,affedave,affedavid,afidaved,affavidit,afidated,afidavite,afodivid,affidated,afadiadid,affidaphet,affidatet,athadiet,afidabit,affidait,afadated,affadivit,affadavit,afadivite,affidavid,affadapfed,affdavit,aphedavid,athadavit,adivide,afdavit,afedavit,afadiatet,alpadavid,afadaviate,affadivid,aftedavid,affadavite,affadavate,apadenment,aphadavet->affidavit
anverrsy,aneversary,anneversies,anniversity,anavuature,annevarcery,annerfversy,anervery,annaversary,anverserice,annaversery,Anniversary,anivrsary,ananersery,anaversie,anniverserie,annaversity,anifurcaty,anenany,anavirsary,aniversy,anverseary,annervesary,annerverarcy,anaveres,anerviersy,aneversy,aniversary,anivesery,anneversers,anirversary,anniversy,aniversere,aneversere,annaversrey,anavorasy,annversary,aniversiry,anerversurey,Amanversery,anniversery,aniversery,anniversiory,anniversily,anneversary,aneversiary,anaversery,anaversity,anniverserys,anerversary,anniverseray,aniverseray,anniverary,anivessery,anaversarie,aniversity,Annyver,annervirsary,anniversty,annevyercy,aniverusy,anarversieiy,onniver,anaversy,anversity,anaveje,anversicy,anniversay,anerversee,aneversarry,anifersery,anversy,aneversery,annaversiry,annivirsary,annivercery,anvesy,anvertery,annversy,anevers,anniverisy,aneversory,anternesery,avernity,Eenarcrsity,anivarisy,aniverserary,annaverserie,anniversaries,aniversay,anyversary,ananversery,annivesrey,anniversiry,annivesry,anniverscy,annerversery,amryvercary,anneversery,anerversery,anversa,anmersersy,aneversitey,aniversry,aniverserry->anniversary
Ane->And
agenst->agents
eeg,agg->egg

Note: I can paste more example lines if wanted.

Answer

apply can be used to invoke a function on each row and is much faster than a for loop (vectorized functions are even faster). I’ve done a few things to make life easier and more performant:

• convert your text file into a dict. This will be more performant and easier to work with than raw text.
• put all the corruption logic in a function. This will be easier to maintain and allows us to use apply
• cleaned up/modified the logic a bit. What I show below is not exactly what you asked but should be easy to adapt.

ok, here is the code:

import io
import random

# this generates a dict {'word1':['list', 'of', 'misspellings'],} where s is a string copied above file
df2 = pd.DataFrame(io.StringIO(s), columns=["subs"])
sub_dict = df2.subs.str.strip().str.split("->", expand=True).set_index(1)[0].str.split(",").to_dict()
sub_dict["fake"] = ["fak", "fkae", "fke"]
sub_dict["tell"] = ["tel"]
sub_dict["this"] = ["tis", "htsi"]
sub_dict["data"] = ["dat", "dta"]


def corrupt(sentence, sub_dict, p=0.5):
    # logic is similar but not identical to your code

    for k, v in sub_dict.items():
        if k in sentence and random.random() <= p:
            
             corrupted_word = random.choice(v)
             sentence = sentence.replace(k, corrupted_word)
    return sentence

Now the apply bit:

df["corrupted"] = df.Sentence.apply(lambda sentence: corrupt(sentence, sub_dict))

# works as expected, see second sentence
   Forname Surname                                         Sentence                                        corrupted
0      Bob   Smith   Hi, this is a perfectly constructred sentence!   Hi, this is a perfectly constructred sentence!
1    Alice   Smith                  Can you tell this is fake data?                    Can you tel htsi is fake dta?
2     John   Smith                          This poster needs help!                          This poster needs help!
3  Michael   Smith  Apparently, this poster is sturggling a bit LOL  Apparently, this poster is sturggling a bit LOL
4   Daniel   Smith                         More fake data here; ok.                           More fke dat here; ok.
5    Sarah   Smith           Will need to think up of better ideas.           Will need to think up of better ideas.
6  Matthew   Smith                   Love a good bit of Python, me.                   Love a good bit of Python, me.
7     Jane   Smith               Is this a sentence?! (I think so).               Is this a sentence?! (I think so).
8    Peter   Smith                           Remarkable - isn't it?                           Remarkable - isn't it?
9    Chloe   Smith       Foo Bar... that's all that is left to say.       Foo Bar... that's all that is left to say.

now lets compare performance with a for-loop:

df_test1 = df.sample(n=10000, replace=True)
df_test2 = df.sample(n=10000, replace=True)
def loop(df):
    for idx, string in enumerate(df['Sentence']):
        corrupted_sentence = corrupt(string, sub_dict)
        df['Sentence'][idx] = corrupted_sentence


%timeit df_test1.Sentence.apply(lambda sentence: corrupt(sentence, sub_dict))
# 36.5 ms ± 1.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)


%timeit loop(df_test2)
# 5.19 s ± 98.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

woohoo! It’s way faster.