text: import boto3
  
  # import pandas and assign to the popular 'pd' convention
  import pandas as pd 
  
  # Create an S3 client
  s3 = boto3.client('s3')
  
  # Creates a variable with the bucket name
  bucket_name = '<insert a unique bucket name>' 
  
  # Creates a new bucket 
  s3.create_bucket(Bucket=bucket_name)
  
  # Create a list of file names
  filenames_list = ['doc-topics.csv', 'topic-terms.csv']
  
  # Iterates on each file in the  filenames_list
  for filename in filenames_list:
  	# Uploads each CSV to the created bucket
  	s3.upload_file(filename, bucket_name, filename)
  	# checks if the filename is 'doc-topics.csv'
  	if filename == 'doc-topics.csv':
  		# gets the 'doc-topics.csv' file as an object
  		obj = s3.get_object(Bucket=bucket_name, Key=filename)
  		# reads the csv and assigns to doc_topics 
  		doc_topics = pd.read_csv(obj['Body'])
  		
  	else:
  		obj = s3.get_object(Bucket=bucket_name, Key=filename)
  		topic_terms = pd.read_csv(obj['Body'])
  		
  
  # merge files on topic column to obtain the most common terms per document
  merged_df = pd.merge(doc_topics, topic_terms, on='topic')
  
  # print the merged_df to the console
  print(merged_df) 
  