Need Help with this peace of code.PLease Exlain Each line of the code.

[AkashDubey]

#find sentences containing HTML tags
i=0;
for sent in final['Text'].values:
    if (len(re.findall('<.*?>', sent))):
        print(i)
        print(sent)
        break;
    i += 1;   

[AkashDubey]

import re
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer

stop = set(stopwords.words('english')) #set of stopwords
sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer

def cleanhtml(sentence): #function to clean the word of any html-tags
    cleanr = re.compile('<.*?>')
    cleantext = re.sub(cleanr, ' ', sentence)
    return cleantext
def cleanpunc(sentence): #function to clean the word of any punctuation or special characters
    cleaned = re.sub(r'[?|!|\'|"|#]',r'',sentence)
    cleaned = re.sub(r'[.|,|)|(|\|/]',r' ',cleaned)
    return  cleaned
print(stop)
print('************************************')
print(sno.stem('tasty'))

[AkashDubey]

#Code for implementing step-by-step the checks mentioned in the pre-processing phase
# this code takes a while to run as it needs to run on 500k sentences.
i=0
str1=' '
final_string=[]
all_positive_words=[] # store words from +ve reviews here
all_negative_words=[] # store words from -ve reviews here.
s=''
for sent in final['Text'].values:
    filtered_sentence=[]
    #print(sent);
    sent=cleanhtml(sent) # remove HTMl tags
    for w in sent.split():
        for cleaned_words in cleanpunc(w).split():
            if((cleaned_words.isalpha()) & (len(cleaned_words)>2)):    
                if(cleaned_words.lower() not in stop):
                    s=(sno.stem(cleaned_words.lower())).encode('utf8')
                    filtered_sentence.append(s)
                    if (final['Score'].values)[i] == 'positive': 
                        all_positive_words.append(s) #list of all words used to describe positive reviews
                    if(final['Score'].values)[i] == 'negative':
                        all_negative_words.append(s) #list of all words used to describe negative reviews reviews
                else:
                    continue
            else:
                continue 
    #print(filtered_sentence)
    str1 = b" ".join(filtered_sentence) #final string of cleaned words
    #print("***********************************************************************")
    
    final_string.append(str1)
    i+=1

[snippsat]

I have merge your 3 threads,it was a close call with delete
We are not gone explain what each line dos.
You have to do the effort,it there are certain lines you wonder about ask about them.

[AkashDubey]

Then, Please give an idea of what all the above three code snippets do briefly explaining each code snippet in detail.

---

I have merge your 3 threads,it was a close call with delete
We are not gone explain what each line dos.
You have to do the effort,it there are certain lines you wonder about ask about them.

Then, Please give an idea of what all the above three code snippets do briefly explaining each code snippet in detail.

[sparkz_alot]

The code is already very well documented as to what it does. As was pointed out, we are not going to explain the purpose of each line. You might want to start with finding a tutorial for Python beginners to learn the basics of the language.