Add update_columns and update_columns_metadata methods to metadata

[frances-h]

Problem Description

As a user, it would be nice if I could update multiple columns in my metadata at once, instead of having to use update_column for every column.

metadata = SingleTableMetadata.detect_from_dataframe(data)
metadata.update_column('col1', sdtype='numerical')
metadata.update_column('col2', sdtype='numerical')
metadata.update_column('col3', sdtype='numerical')

I'd like to be able to update multiple columns that have the same metadata kwargs at once, or pass in metadata updates to multiple columns as a dictionary.

Expected behavior

Add two methods to the metadata, update_columns which updates all the given columns with the same metadata kwargs and update_columns_metadata which updates columns using metadata from the input dictionary. We should validate the inputs to verify that the columns exist and the provided arguments are valid.

Single Table API

• update_columns(column_names, **metadata_kwargs)
  • column_names (list[str]) - a list of column names that should be updated
  • **metadata_kwargs - Any key word arguments that describe metadata for the column (i.e. sdtype, pii, computer_representation, etc.)

metadata = SingleTableMetadata.detect_from_dataframe(data)
metadata.update_columns(['col1', 'col2', 'col3'], sdtype='numerical')

• update_columns_metadata(column_metadata_dict)
  • column_metadata_dict (dict) - a dictionary mapping column names to column metadata (i.e. {'amount': {'sdtype': 'numerical'}}

metadata = SingleTableMetadata.detect_from_dataframe(data)
metadata.update_columns_metadata({
    'col1': {'sdtype': 'numerical'},
    'col2': {'sdtype': 'address', 'pii': True},
    'col3': {'sdtype': 'id', 'regex': 'ID_[0-9]{3}'}
})

Multi Table

• update_columns(table_name, column_names, **metadata_kwargs)
  • table_name (str) - the name of the target table
  • column_names (list[str]) - a list of column names that should be updated
  • **metadata_kwargs - Any key word arguments that describe metadata for the column (i.e. sdtype, pii, computer_representation, etc.)

metadata = .detect_from_dataframes(data)
metadata.update_columns(
 'users',
 ['col1', 'col2', 'col3'],
 sdtype='numerical'
)

• update_columns_metadata(table_name, column_metadata_dict)
  • table_name (str) - the name of the target table
  • column_metadata_dict (dict) - a dictionary mapping column names to column metadata (i.e. {'amount': {'sdtype': 'numerical'}}

metadata = MultiTableMetadata.detect_from_dataframes(data)
metadata.update_columns_metadata(
  table_name='users',
  column_metadata_dict={
    'col1': {'sdtype': 'numerical'},
    'col2': {'sdtype': 'address', 'pii': True},
    'col3': {'sdtype': 'id', 'regex': 'ID_[0-9]{3}'}
  }
)