merge_machine/preprocess_fields_v3.py
def normalize_values(tab, params):
	''' Normalizes the values in each column whose type has been identified, and returns the input array after adding the
		resulting new columns.

		Based on those values, more than one additional column may be added to a given input field:
		- extracted components for a composite type
		- variants for a data type within a domain rich in lexical variations like synonyms, etc. '''
	modified = pd.DataFrame(False, index=tab.index, columns=tab.columns)
	fields = parse_fields_from_Panda(tab)
	# Fetch results of previous step (type inference) so as to avoid duplicative work
	trusted_types = params['column_types']
	fields.match_by_types(trusted_types)
	for (originalField, newCol) in fields.normalize_values_in_place(trusted_types):
		modified[originalField] = (tab[originalField] != newCol)
		tab.loc[:, originalField] = newCol
	return tab, modified
 Normalizes the values in each column whose type has been identified, and returns the input array after adding the
		resulting new columns.

		Based on those values, more than one additional column may be added to a given input field:
		- extracted components for a composite type
		- variants for a data type within a domain rich in lexical variations like synonyms, etc. 
Normalizes the values in each column whose type has been identified, and returns the input array after adding the
resulting new columns.

Based on those values, more than one additional column may be added to a given input field:
extracted components for a composite type
variants for a data type within a domain rich in lexical variations like synonyms, etc.
Normalizes the values in each column whose type has been identified and returns the input array after adding the resulting new columns . Based on those values more than one additional column may be added to a given input field : extracted components for a composite type variants for a data type within a domain rich in lexical variations like synonyms etc .
def normalize_values(tab, params):
	modified = pd.DataFrame(False, index=tab.index, columns=tab.columns)
	fields = parse_fields_from_Panda(tab)
	trusted_types = params['column_types']
	fields.match_by_types(trusted_types)
	for (originalField, newCol) in fields.normalize_values_in_place(trusted_types):
		modified[originalField] = (tab[originalField] != newCol)
		tab.loc[:, originalField] = newCol
	return tab, modified
def normalize_values ( tab , params ) : modified = pd . DataFrame ( False , index = tab . index , columns = tab . columns ) fields = parse_fields_from_Panda ( tab ) trusted_types = params [ 'column_types' ] fields . match_by_types ( trusted_types ) for ( originalField , newCol ) in fields . normalize_values_in_place ( trusted_types ) : modified [ originalField ] = ( tab [ originalField ] != newCol ) tab . loc [ : , originalField ] = newCol return tab , modified
Normalizes the values in each column whose type has been identified, and returns the input array after adding the
resulting new columns.
Normalizes the values in each column whose type has been identified and returns the input array after adding the resulting new columns .
''' Normalizes the values in each column whose type has been identified, and returns the input array after adding the
		resulting new columns.

		Based on those values, more than one additional column may be added to a given input field:
		- extracted components for a composite type
		- variants for a data type within a domain rich in lexical variations like synonyms, etc. ''' # Fetch results of previous step (type inference) so as to avoid duplicative work
tab
params