old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  
  """Config file for the cleaning - plotting and notebook process"""
  
  
  class CleaningConfig:
  
      # Unprocessed dataset
      raw_data = './uk_2017/data/raw_results-survey245554.csv'
      # load the different answers to questions to classify questions based on that
      question_file = './../survey_creation/uk_17/uk_17.csv'
      answer_folder = './../survey_creation/uk_17/listAnswers'
      # Location for the json file of all questions
      json_to_plot_location = './uk_2017/data/to_plot.json'
      cleaned_df_location = './uk_2017/data/cleaned_data.csv'
      section_nbr_to_keep_after = 1
      country_to_keep = 'United Kingdom'
      count_na = True
      normalise = False
  
  
  class CountingConfig(CleaningConfig):
      # Folder where to store the dataframe in a csv format
      folder_df = './uk_2017/output/'
  
  
  class PlottingConfig(CountingConfig):
  
      plot_na = False
      normalise = True
      # Different than normalise, add a freq_table with percentage
      # in addition of the table with counts
      show_percent = True
  
  
  class NotebookConfig(PlottingConfig):
  
      notebook_folder = './uk_2017/'
      notebook_filename = 'notebook.ipynb'
      notebook_html = 'notebook.html'
      allow_errors = True
      to_import = ['import pandas as pd',
                   'import numpy as np',
                   'import matplotlib',
                   'import matplotlib.pyplot as plt',
                   'from IPython.display import display',
                   'import IPython.core.display as di',
                   'from include.config import CleaningConfig, PlottingConfig, NotebookConfig',
                   'from include.counting import get_count, get_percentage',
                   'from include.plotting import get_plot, display_side_by_side',
                   'from include.likertScalePlot import likert_scale',
                   'from include.textCleaning import wordcloud']
      processing_options = {'metadata': {'path': './'}}
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  
  """Config file for the cleaning - plotting and notebook process"""
  
  
  class CleaningConfig:
      def __init__(self, country, year):
          self.country = country
          self.year = year
  
      # Unprocessed dataset
      raw_data = './uk_2017/data/raw_results-survey245554.csv'
      # load the different answers to questions to classify questions based on that
      question_file = './../survey_creation/uk_17/uk_17.csv'
      answer_folder = './../survey_creation/uk_17/listAnswers'
      # Location for the json file of all questions
      json_to_plot_location = './uk_2017/data/to_plot.json'
      cleaned_df_location = './uk_2017/data/cleaned_data.csv'
      section_nbr_to_keep_after = 1
      country_to_keep = 'United Kingdom'
      count_na = True
      normalise = False
  
  
  class CountingConfig(CleaningConfig):
      # Folder where to store the dataframe in a csv format
      folder_df = './uk_2017/output/'
  
  
  class PlottingConfig(CountingConfig):
  
      plot_na = False
      normalise = True
      # Different than normalise, add a freq_table with percentage
      # in addition of the table with counts
      show_percent = True
  
  
  class NotebookConfig(PlottingConfig):
      notebook_folder = './uk_2017/'
      notebook_filename = 'notebook.ipynb'
      notebook_html = 'notebook.html'
      allow_errors = True
      to_import = ['import pandas as pd',
                   'import numpy as np',
                   'import matplotlib',
                   'import matplotlib.pyplot as plt',
                   'from IPython.display import display',
                   'import IPython.core.display as di',
                   'from include.config import CleaningConfig, PlottingConfig, NotebookConfig',
                   'from include.counting import get_count, get_percentage',
                   'from include.plotting import get_plot, display_side_by_side',
                   'from include.likertScalePlot import likert_scale',
                   'from include.textCleaning import wordcloud']
      processing_options = {'metadata': {'path': './'}}
  
subject: add the init in the config file