original_docstring: Create the ESCO Dictionary with keys as all known occupations in portuguese and english.
      --> Create a dictionary mapping occupations to code, iscoGroup and skills
  
      Args:
          merged_occupations (pd.DataFrame): merged occupattions (english and portuguese) 
          merged_df (pd.DataFrame): esco merged skills with its relation to occupations through occupationUrr 
  
      Returns:
          dict: Esco Dictionary with keys as preferred and alt labels from english and portuguese occupations
      
docstring: Create the ESCO Dictionary with keys as all known occupations in portuguese and english.
  > Create a dictionary mapping occupations to code, iscoGroup and skills
docstring_tokens: Create the ESCO Dictionary with keys as all known occupations in portuguese and english . > Create a dictionary mapping occupations to code iscoGroup and skills
code: def createEscoDict(merged_occupations, merged_df):
      """Create the ESCO Dictionary with keys as all known occupations in portuguese and english.
      --> Create a dictionary mapping occupations to code, iscoGroup and skills
  
      Args:
          merged_occupations (pd.DataFrame): merged occupattions (english and portuguese) 
          merged_df (pd.DataFrame): esco merged skills with its relation to occupations through occupationUrr 
  
      Returns:
          dict: Esco Dictionary with keys as preferred and alt labels from english and portuguese occupations
      """
      esco_dict = {}
      for label in merged_occupations[['preferredLabel_x','altLabels_x', 'preferredLabel_y', 'altLabels_y','occupationUri', 'iscoGroup']].itertuples():
          esco_dict[normalizeESCOdict(label.preferredLabel_x)] = {
              'occupationUri':label.occupationUri, 'iscoGroup':label.iscoGroup, 
              'occupation': normalizeESCOdict(label.preferredLabel_x)
              }
          esco_dict[normalizeESCOdict(label.altLabels_x)] = {
              'occupationUri':label.occupationUri, 'iscoGroup':label.iscoGroup, 
              'occupation': normalizeESCOdict(label.preferredLabel_x)
              }
          esco_dict[normalizeESCOdict(label.preferredLabel_y)] = {
              'occupationUri':label.occupationUri, 'iscoGroup':label.iscoGroup, 
              'occupation': normalizeESCOdict(label.preferredLabel_x)
              }
          esco_dict[normalizeESCOdict(label.altLabels_y)] = {
              'occupationUri':label.occupationUri, 'iscoGroup':label.iscoGroup, 
              'occupation': normalizeESCOdict(label.preferredLabel_x)
              }
  
      esco_aux_dict = esco_dict.copy() 
      for key in esco_aux_dict:
          esco_dict[key]['skills'] = merged_df.loc[
              merged_df['occupationUri']==esco_dict[key]['occupationUri']
              ]['preferredLabel'].to_list()
      
      return esco_dict
code_tokens: def createEscoDict ( merged_occupations , merged_df ) : esco_dict = { } for label in merged_occupations [ [ 'preferredLabel_x' , 'altLabels_x' , 'preferredLabel_y' , 'altLabels_y' , 'occupationUri' , 'iscoGroup' ] ] . itertuples ( ) : esco_dict [ normalizeESCOdict ( label . preferredLabel_x ) ] = { 'occupationUri' : label . occupationUri , 'iscoGroup' : label . iscoGroup , 'occupation' : normalizeESCOdict ( label . preferredLabel_x ) } esco_dict [ normalizeESCOdict ( label . altLabels_x ) ] = { 'occupationUri' : label . occupationUri , 'iscoGroup' : label . iscoGroup , 'occupation' : normalizeESCOdict ( label . preferredLabel_x ) } esco_dict [ normalizeESCOdict ( label . preferredLabel_y ) ] = { 'occupationUri' : label . occupationUri , 'iscoGroup' : label . iscoGroup , 'occupation' : normalizeESCOdict ( label . preferredLabel_x ) } esco_dict [ normalizeESCOdict ( label . altLabels_y ) ] = { 'occupationUri' : label . occupationUri , 'iscoGroup' : label . iscoGroup , 'occupation' : normalizeESCOdict ( label . preferredLabel_x ) } esco_aux_dict = esco_dict . copy ( ) for key in esco_aux_dict : esco_dict [ key ] [ 'skills' ] = merged_df . loc [ merged_df [ 'occupationUri' ] == esco_dict [ key ] [ 'occupationUri' ] ] [ 'preferredLabel' ] . to_list ( ) return esco_dict
short_docstring: Create the ESCO Dictionary with keys as all known occupations in portuguese and english.
short_docstring_tokens: Create the ESCO Dictionary with keys as all known occupations in portuguese and english .
comment: """Create the ESCO Dictionary with keys as all known occupations in portuguese and english.
      --> Create a dictionary mapping occupations to code, iscoGroup and skills
  
      Args:
          merged_occupations (pd.DataFrame): merged occupattions (english and portuguese) 
          merged_df (pd.DataFrame): esco merged skills with its relation to occupations through occupationUrr 
  
      Returns:
          dict: Esco Dictionary with keys as preferred and alt labels from english and portuguese occupations
      """