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Tokenizing with out changing the length

I have a text file with comments and I need to do classification with it. The text file is a list has a length of 1000. But after tokenization, stem, and stopwords the length of gets bigger. How can I tokenize with out changing the length?
Here is my code:
!pip install nltk
import nltk
import pandas as pd
data = open('X_train.txt',encoding='utf8').readlines()
nltk.download('punkt')
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
from nltk import word_tokenize
for word in data:
if word in stopwords.words('english'):
data.remove(word)
words_after = []
for w in data:
tokens = word_tokenize(w)
for token in tokens:
stemmed_word = ps.stem(token)
words_after.append(stemmed_word)
print(len(words_after))

Unstructured data, NLP Lemmatize Book Review

Here I have m trying to read the content let's say 'book1.txt' and here I have to remove all the special characters and punctuation marks and word tokenise the content using nltk's word tokeniser.
Lemmatize those token using wordnetLemmatizer
And write those token into csv file one by one.
Here is the code I m using which obviously is not working but just need some suggestion on this please.
import nltk
from nltk.stem import WordNetLemmatizer
import csv
from nltk.tokenize import word_tokenize
file_out=open('data.csv','w')
with open('book1.txt','r') as myfile:
for s in myfile:
words = nltk.word_tokenize(s)
words=[word.lower() for word in words if word.isalpha()]
for word in words:
token=WordNetLemmatizer().lemmatize(words,'v')
filtered_sentence=[""]
for n in words:
if n not in token:
filtered_sentence.append(""+n)
file_out.writelines(filtered_sentence+["\n"])

There's some issues here, most notably with the last 2 for loops.
The way you are doing it made it write it as follows:
word1
word1word2
word1word2word3
word1word2word3word4
........etc
I'm guessing that is not the expected output. I'm assuming the expected output is:
word1
word2
word3
word4
........etc (without creating duplicates)
I applied the code below to a 3 paragraph Cat Ipsum file. Note that I changed some variable names due to my own naming conventions.
import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from pprint import pprint
# read the text into a single string.
with open("book1.txt") as infile:
text = ' '.join(infile.readlines())
words = word_tokenize(text)
words = [word.lower() for word in words if word.isalpha()]
# create the lemmatized word list
results = []
for word in words:
# you were using words instead of word below
token = WordNetLemmatizer().lemmatize(word, "v")
# check if token not already in results.
if token not in results:
results.append(token)
# sort results, just because :)
results.sort()
# print and save the results
pprint(results)
print(len(results))
with open("nltk_data.csv", "w") as outfile:
outfile.writelines(results)

Importing stopword dictionary to python

How can I import a specific stopword dictionary (excel sheet) into Python and run it additionally to the nltk stopword list? Currently my stopword section looks like this:
# filter out stop words
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
words = [w for w in words if not w in stop_words]
Thanks in advance!

You can import an excel sheet using the pandas library. This example assumes that your stopwords are located in the first column, one word per row. Afterwards, create the union of the nltk stopwords and your own stopwords:
import pandas as pd
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
# check pandas docs for more info on usage of read_excel
custom_words = pd.read_excel('your_file.xlsx', header=None, names=['mywords'])
# union of two sets
stop_words = stop_words | set(custom_words['mywords'])
words = [w for w in words if not w in stop_words]

How to add more stopwords in nltk list?

I have the following code. I have to add more words in nltk stopword list. After i run thsi, it does not add the words in the list
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
import string
stop = set(stopwords.words('english'))
new_words = open("stopwords_en.txt", "r")
new_stopwords = stop.union(new_word)
exclude = set(string.punctuation)
lemma = WordNetLemmatizer()
def clean(doc):
stop_free = " ".join([i for i in doc.lower().split() if i not in new_stopwords])
punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
return normalized
doc_clean = [clean(doc).split() for doc in emails_body_text]

Don't do things blindly. Read in your new list of stopwords, inspect it to see that it's right, then add it to the other stopword list. Start with the code suggested by #greg_data, but you'll need to strip newlines and maybe do other things -- who knows what your stopwords file looks like?
This might do it, for example:
new_words = open("stopwords_en.txt", "r").read().split()
new_stopwords = stop.union(new_words)
PS. Don't keep splitting and joining your document; tokenize once and work with the list of tokens.

Stemming words with NLTK (python)

I am new to Python text processing, I am trying to stem word in text document, has around 5000 rows.
I have written below script
from nltk.corpus import stopwords # Import the stop word list
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer('english')
def Description_to_words(raw_Description ):
# 1. Remove HTML
Description_text = BeautifulSoup(raw_Description).get_text()
# 2. Remove non-letters
letters_only = re.sub("[^a-zA-Z]", " ", Description_text)
# 3. Convert to lower case, split into individual words
words = letters_only.lower().split()
stops = set(stopwords.words("english"))
# 5. Remove stop words
meaningful_words = [w for w in words if not w in stops]
# 5. stem words
words = ([stemmer.stem(w) for w in words])
# 6. Join the words back into one string separated by space,
# and return the result.
return( " ".join( meaningful_words ))
clean_Description = Description_to_words(train["Description"][15])
But when I test results words were not stemmed , can anyone help me to know what is issue , I am doing something wrong in "Description_to_words" function
And, when I execute stem command separately like below it works.
from nltk.tokenize import sent_tokenize, word_tokenize
>>> words = word_tokenize("MOBILE APP - Unable to add reading")
>>>
>>> for w in words:
... print(stemmer.stem(w))
...
mobil
app
-
unabl
to
add
read

Here's each step of your function, fixed.
Remove HTML.
Description_text = BeautifulSoup(raw_Description).get_text()
Remove non-letters, but don't remove whitespaces just yet. You can also simplify your regex a bit.
letters_only = re.sub("[^\w\s]", " ", Description_text)
Convert to lower case, split into individual words: I recommend using word_tokenize again, here.
from nltk.tokenize import word_tokenize
words = word_tokenize(letters_only.lower())
Remove stop words.
stops = set(stopwords.words("english"))
meaningful_words = [w for w in words if not w in stops]
Stem words. Here is another issue. Stem meaningful_words, not words.
return ' '.join(stemmer.stem(w) for w in meaningful_words])