Codecademy export

script.py

# These are the emails you will be censoring. '''Censors emails, to keep valuable information hidden'''
#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):
  '''Censors a single word/phrase and returns email'''
  censor = ''
  for i in range(len(word)):
    if word[i] == ' ':
      censor += ' '
    else:
      censor += '*'
  return email.replace(word, censor)

def censor_proprietary_terms(email):
  '''Censors all proprietary terms and returns email'''
  for term in proprietary_terms[::-1]: 
    email = censor_word(email, term)
    email = censor_word(email, term.title())
  return email

def censor_negative_words_and_p_terms(email):
  '''Censors al prioprietary terms and negative words and returns email'''
  email = censor_proprietary_terms(email) #censor_proprietary_terms
  email_words = email.split(' ') #List of each word on email 
  email_words = email.split('\n')
  for negative_word in negative_words:#For word in negative_words
    count = 0
    for word in email_words:
      if (negative_word in word) or (negative_word.title() in word):
        count += 1
    if count > 2:
      email = censor_word(email, negative_word)
      email = censor_word(email, negative_word.title())
  return email

def censor_it_all(email):
  '''Censor all proprietary terms, negative words and each word that comes before and after the censored word/phrase, returns email'''
  email = censor_proprietary_terms(email)#Censor proprietary terms
  for negative_word in negative_words:  #Censor negative words
    email = censor_word(email, negative_word)
    email = censor_word(email, negative_word.title())
    
  email_updates = []
  email_paragraphs = email.split('\n') #List of each word on email 
  for paragraph in email_paragraphs:
    paragraph_words = paragraph.split(' ')
    count = 0
    for word in range(0, len(paragraph_words)-1):
      if ('*' in paragraph_words[word]) and (count % 2 == 0):
        count += 1
        paragraph_words[word-1] = '*'*len(paragraph_words[word-1])
        paragraph_words[word+1] = '*'*len(paragraph_words[word+1])
    paragraph_words = ' '.join(paragraph_words)
    email_updates.append(paragraph_words)
      
    
  email_updates = '\n'.join(email_updates)
  return email_updates


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"]

email = censor_it_all(email_four)
print(email)