new_contents: import sys
  import numpy as np
  import pandas as pd
  
  users = pd.read_csv('../datasets/processed/preprocessed.csv')
  
  users.drop('Unnamed: 0', axis=1, inplace=True)
  
  to_remove = users.isnull().sum().loc[users.isnull().sum() > 275542].index
  
  users.drop(to_remove, axis=1, inplace=True)
  
  sys.path.append('..')
  from utils.data_loading import load_sessions_data
  sessions = load_sessions_data()
  
  elapsed_secs_sum = sessions.groupby('user_id')['secs_elapsed'].sum()
  elapsed_secs_sum.name = 'elapsed_secs_sum'
  elapsed_secs_average = sessions.groupby('user_id')['secs_elapsed'].mean()
  elapsed_secs_average.name = 'elapsed_secs_average'
  
  users = pd.concat([users, elapsed_secs_sum], axis=1)
  users = pd.concat([users, elapsed_secs_average], axis=1)
  
  min_secs_elapsed = sessions.groupby('user_id')['secs_elapsed'].min()
  min_secs_elapsed.name = 'min_secs_elapsed'
  max_secs_elapsed = sessions.groupby('user_id')['secs_elapsed'].max()
  max_secs_elapsed.name = 'max_secs_elapsed'
  users = pd.concat([users, min_secs_elapsed], axis=1)
  users = pd.concat([users, max_secs_elapsed], axis=1)
  
  first_quantile = sessions.groupby('user_id')['secs_elapsed'].quantile(0.25)
  first_quantile.name = 'first_quantile'
  second_quantile = sessions.groupby('user_id')['secs_elapsed'].quantile(0.5)
  second_quantile.name = 'second_quantile'
  third_quantile = sessions.groupby('user_id')['secs_elapsed'].quantile(0.75)
  third_quantile.name = 'third_quantile'
  fourth_quantile = sessions.groupby('user_id')['secs_elapsed'].quantile(0.9)
  fourth_quantile.name = 'fourth_quantile'
  users = pd.concat([users, first_quantile], axis=1)
  users = pd.concat([users, second_quantile], axis=1)
  users = pd.concat([users, third_quantile], axis=1)
  users = pd.concat([users, fourth_quantile], axis=1)
  
  median = sessions.groupby('user_id')['secs_elapsed'].median()
  median.name = 'elapsed_secs_median'
  users = pd.concat([users, median], axis=1)
  std = sessions.groupby('user_id')['secs_elapsed'].std()
  std.name = 'elapsed_secs_std'
  users = pd.concat([users, std], axis=1)
  var = sessions.groupby('user_id')['secs_elapsed'].var()
  var.name = 'elapsed_secs_var'
  users = pd.concat([users, var], axis=1)
  skew = sessions.groupby('user_id')['secs_elapsed'].skew()
  skew.name = 'elapsed_secs_skew'
  users = pd.concat([users, skew], axis=1)
  
  day_pauses = sessions.loc[sessions['secs_elapsed'] > 86400].groupby('user_id').count()['secs_elapsed']
  day_pauses.name = 'day_pauses'
  users = pd.concat([users, day_pauses], axis=1)
  short_sessions = sessions.loc[sessions['secs_elapsed'] < 3600].groupby('user_id').count()['secs_elapsed']
  short_sessions.name = 'short_sessions'
  users = pd.concat([users, short_sessions], axis=1)
  long_sessions = sessions.loc[sessions['secs_elapsed'] > 300000].groupby('user_id').count()['secs_elapsed']
  long_sessions.name = 'long_sessions'
  users = pd.concat([users, long_sessions], axis=1)
  
  first_secs_elapsed = sessions.groupby('user_id')['secs_elapsed'].first()
  first_secs_elapsed.name = 'first_secs_elapsed'
  users = pd.concat([users, first_secs_elapsed], axis=1)
  last_secs_elapsed = sessions.groupby('user_id')['secs_elapsed'].last()
  last_secs_elapsed.name = 'last_secs_elapsed'
  users = pd.concat([users, last_secs_elapsed], axis=1)
  
  users.to_csv('users_with_session.cvs')
  
subject: Add preocessing for elapsed secs