text: import pandas as pd
  
  # import numpy as np
  # import random
  
  # Importing all data
  movies_data = pd.read_csv('ml/movies.csv', sep=',')
  ratings_data = pd.read_csv('ml/ratings.csv', sep=',')
  tags_data = pd.read_csv('ml/tags.csv', sep=',')
  links_data = pd.read_csv('ml/links.csv', sep=',')
  genome_scores_data = pd.read_csv('ml/genome-scores_csv', sep=',')
  genome_tags_data = pd.read_csv('ml/genome-tags.csv', sep=',')
  
  # Genres data
  genre_labels = set()
  for s in movies_data['genres'].str.split('|').values:
      genre_labels = genre_labels.union(set(s))
  
  # Cleaning timestamps
  del ratings_data['timestamp']
  del tags_data['timestamp']
  
  # Cleaing null rows
  tags_data = tags_data.dropna()
  
  # Inner Joins on tables
  movie_ratings_data = movies_data.merge(ratings_data, on = 'movieId', how = 'inner')
  
  print('\n\n')
  
  
  from cassandra.cluster import Cluster
  
  cluster = Cluster()
  session = cluster.connect()
  rows = session.execute('SELECT name, age, email FROM users')
  for user_row in rows:
      print(user_row.name, user_row.age, user_row.email)