<jupyter_start><jupyter_text>Example usageTo use `pytextprep` in a project:  Imports<jupyter_code>import re
from pytextprep.extract_ngram import extract_ngram
from pytextprep.extract_hashtags import extract_hashtags
from pytextprep.remove_punct import remove_punct
from pytextprep.generate_cloud import generate_cloud
import pytextprep
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>Create list of Tweets<jupyter_code>tweets_list = [
    "hello literally everyone",
    "hello #America!",
    "Make America Great Again! @DonalTrump", 
    "It's a new day in #America"]<jupyter_output><empty_output><jupyter_text>Remove punctuation<jupyter_code>cleaned_tweets = remove_punct(tweets_list, skip=["'", "@", "#", '-'])
cleaned_tweets<jupyter_output><empty_output><jupyter_text>Extract ngrams<jupyter_code>extract_ngram(cleaned_tweets, n=3)<jupyter_output><empty_output><jupyter_text>Extract hashtags<jupyter_code>extract_hashtags(tweets=tweets_list)<jupyter_output><empty_output><jupyter_text>Generate word cloud<jupyter_code>fig, wc = generate_cloud(tweets_list)
plt.show()<jupyter_output>[nltk_data] Downloading package stopwords to
[nltk_data]     /Users/melisamaidana/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
{'fraction_non_alphanumeric': 0.10063391442155309, 'fraction_numerical': 0.000792393026941363, 'mean_word_length': 9.525, 'pattern_counts': {'":': 0, '<': 24, '<?xml version=': 0, '>': 24, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '82448', 'license': 'MIT', 'n_tokens_mistral': 481, 'n_tokens_neox': 469, 'n_words': 74, 'path': 'docs/example.ipynb', 'repo_name': 'UBC-MDS/pytextprep', 'size': '1262'}