Codecademy export

script.py

#The open() function is opening the text file that the emails are contained in and the .read() method is allowing us to save their contexts to the following variables:
email_one = open("email_one.txt", "r").read()
email_two = open("email_two.txt", "r").read()
email_three = open("email_three.txt", "r").read()
email_four = open("email_four.txt", "r").read()

def censor_word(email, word):
  email = email.casefold()
  word = word.casefold()
  if word in email:
    email.replace(word, '***')
  return 

def censor_proprietary_terms(email):
  email = email.casefold()
  for term in proprietary_terms:
    term = term.casefold()
    if term in email:
      email.replace(term, '***')
  return

def censor_negative_words(email):
  email = censor_proprietary_terms(email)
  email_words = ' '.split(email)
  for negative_word in negative_words:
    count = 0
    for word in email_words:
      if negative_word in word:
        count += 1
    if (count > 2):
      email.replace(negative_word,'***')
  return email

def censor_it_all(email):
  email = censor_negative_words(email)
  email_censor_split = '***'.split(email)
  email_updated = ''
  count = 0
  for section in email_censor_split:
    count += 1
    section_words = ' '.split(section)
    section_words[-1] = '***'
    if count > 1:
      section_words[0] = '***'
    section_words = ' '.join(section_words)
    section_words += ' *** '
    email_updated += section_words
  return email_updated
      
word = 'learning algorithms'

proprietary_terms = ["she", "personality matrix", "sense of self", "self-preservation", "learning algorithm", "her", "herself"]

negative_words = ["concerned", "behind", "danger", "dangerous", "alarming", "alarmed", "out of control", "help", "unhappy", "bad", "upset", "awful", "broken", "damage", "damaging", "dismal", "distressed", "distressed", "concerning", "horrible", "horribly", "questionable"]