def transform_column_source_data(data):
 data_copy = {}
 for key in iterkeys(data):
  if (pd and isinstance(data[key], (pd.Series, pd.Index))):
   data_copy[key] = transform_series(data[key])
  elif isinstance(data[key], np.ndarray):
   data_copy[key] = transform_array(data[key])
  else:
   data_copy[key] = traverse_data(data[key])
 return data_copy

def transform_column_source_data ( data ) : data_copy = { } for key in iterkeys ( data ) : if ( pd and isinstance ( data [ key ] , ( pd . Series , pd . Index ) ) ) : data_copy [ key ] = transform_series ( data [ key ] ) elif isinstance ( data [ key ] , np . ndarray ) : data_copy [ key ] = transform_array ( data [ key ] ) else : data_copy [ key ] = traverse_data ( data [ key ] ) return data_copy
transform columnsourcedata data to a serialized format args: data : the mapping of names to data columns to transform returns: json compatible dict .