text: # Handling categorical Variables
  
  # importing required libraries
  import pandas as pd
  from sklearn import preprocessing
  """
  # Reading csv file
  df = pd.read_csv('data_restaurant.csv')
  print(df.head())
  
  
  # Extracting categorical features from above dataset
  df1 = df[['Gender', 'Smoker', 'Day', 'Time']]
  
  # Print first 5 records
  print(df1.head())
  
  # Using get_dummies method from pandas to handle categorical features
  print(pd.get_dummies(df1).head())
  
  print(df1.head())
  
  # Using sklearn preprocessing to handle categorical features
  le = preprocessing.LabelEncoder()
  df2 = df1.apply(le.fit_transform)
  
  print(df2.head())
  """
  # Categorical module to get categorical Variables from given dataset
  
  
  def get_categorical():
      fil = input('Enter the filename:')
      df = pd.read_csv(fil + '.csv')
      cate = df.select_dtypes(exclude=['float64', 'int64'])
      return cate
  
  
  df_cat = get_categorical()
  
  # Printing first 5 records
  print(df_cat.head())
  
  # Using get_dummies method from pandas to handle categorical features
  print(pd.get_dummies(df_cat).head())
  
  print(df_cat.head())
  
  # Using sklearn preprocessing to handle categorical features
  le = preprocessing.LabelEncoder()
  print(df_cat.apply(le.fit_transform))
  