<jupyter_start><jupyter_text>job1Entry Level Sales Consultant[link]https://www.indeed.com/q-Entry-Level-Consulting-l-Miami,-FL-jobs.html?advn=5881671747126226&vjk=2a92760904dc0a71  job2Big Data Consultant[link]<jupyter_code>import xlwt        
from collections import Counter        
from nltk.corpus import stopwords
stop = set(stopwords.words('english'))
  
book = xlwt.Workbook() # create a new excel file
sheet_test = book.add_sheet('word_count') # add a new sheet
i = 0
sheet_test.write(i,0,'word') # write the header of the first column
sheet_test.write(i,1,'count') # write the header of the second column
sheet_test.write(i,2,'ratio') # write the header of the third column
    
with open('job_one.txt','r',encoding='utf-8', errors = 'ignore') as text_word: # define the location of your txt file
     
    # convert all the word into lower cases
    # filter out stop words
    word_list = [i for i in text_word.read().lower().split() if i not in stop]
    word_total = word_list.__len__()
     
    count_result =  Counter(word_list)
    for result in count_result.most_common(10):
        i = i+1 
        sheet_test.write(i,0,result[0])
        sheet_test.write(i,1,result[1])
        sheet_test.write(i,2,(result[1]/word_total))
    
book.save('job_one.xls')# define the location of your excel file<jupyter_output><empty_output><jupyter_text><jupyter_code>import xlwt        
from collections import Counter        
from nltk.corpus import stopwords
stop = set(stopwords.words('english'))
  
book = xlwt.Workbook() # create a new excel file
sheet_test = book.add_sheet('word_count') # add a new sheet
i = 0
sheet_test.write(i,0,'word') # write the header of the first column
sheet_test.write(i,1,'count') # write the header of the second column
sheet_test.write(i,2,'ratio') # write the header of the third column
    
with open('job_two.txt','r',encoding='utf-8', errors = 'ignore') as text_word: # define the location of your txt file
     
    # convert all the word into lower cases
    # filter out stop words
    word_list = [i for i in text_word.read().lower().split() if i not in stop]
    word_total = word_list.__len__()
     
    count_result =  Counter(word_list)
    for result in count_result.most_common(10):
        i = i+1 
        sheet_test.write(i,0,result[0])
        sheet_test.write(i,1,result[1])
        sheet_test.write(i,2,(result[1]/word_total))
    
book.save('job_two.xls')# define the location of your excel file<jupyter_output><empty_output><jupyter_text>Q5<jupyter_code>with open ('job_one.txt','r',encoding='utf-8',errors = 'ignore')as job_one: #this part we haven't covered so you prob dont understand yet
    with open('job_two.txt','r',encoding='utf-8',errors = 'ignore')as job_two:
        jobone_str = job_one.read()
        jobtwo_str = job_two.read()
       
              
        jobone_set = set(jobone_str.split())  
        jobtwo_set = set(jobtwo_str.split())
        
        print(jobone_set.difference(jobtwo_set)) 
        print(jobtwo_set.difference(jobone_set))<jupyter_output>{'includes', 'development,', 'would', 'employer', 'perfect', 'Description', 'gender,', 'product', 'win.', 'parking', 'sales', 'customers', 'request', 'beer', 'national', 'growth;', 'screening,', 'Gainsight,', 'Technology', 'trusted', 'adopting', 'engaging', 'free', 'will:', 'product,', 'require', 'Entry', 'Other', 'Indeed', 'high', 'company', 'taking', 'sessions', 'any', 'account', 'serve', 'Player.', 'countries.', 'sex,', 'relationship', 'opportunity', 'onboarding', 'training', 'Must', 'Take', 'are.', 'Individual', 'satisfaction.', 'presence', 'Zendesk', 'Beer', 'characteristic', 'industries', 'uncapped', 'employment', 'providers', 'Eager', 'building', 'expanding', 'government,', 'regard', 'weekly,', 'retention', 'worldwide', 'drink', '(portion', 'ensuring', 'corporate', 'Development', 'success', 'monthly,', 'Level', 'venture', 'plus', 'educate,', 'Health', 'FL', 'Own', 'Sales,', 'approach', 'Participate', 'escalations', '#1', 'let', 'commission.', 'Establish', 'while', 'directly', 'g[...]<jupyter_text>Q4<jupyter_code>from fuzzywuzzy import fuzz
with open ('job_one.txt','r',encoding='utf-8',errors = 'ignore')as job_one: #this part we haven't covered so you prob dont understand yet
    with open('job_two.txt','r',encoding='utf-8',errors = 'ignore')as job_two:
        jobone_str = job_one.read()
        jobtwo_str = job_two.read()
        
        print(fuzz.token_sort_ratio(jobone_str,jobtwo_str))
              
    #    jobone_set = set(jobone_str.split())  
     #   jobtwo_set = set(jobtwo_str.split())
        
   #    print(jobone_set.difference(jobtwo_set)) 
    #    print(jobtwo_set.difference(jobone_set))<jupyter_output>1
StarcoderdataJupyterStructuredCleanDedup