we'll take the food namesgroupidand manufacturerin [ ]info_keys ['description''group''id''manufacturer'in [ ]info dataframe(dbcolumns=info_keysin [ ]info[: out[ ]description cheesecaraway cheesecheddar cheeseedam cheesefeta cheesemozzarellapart skim milk group dairy and egg products dairy and egg products dairy and egg products dairy and egg products dairy and egg products id manufacturer in [ ]info out[ ]int index entries to data columnsdescription non-null values group non-null values id non-null values manufacturer non-null values dtypesint ( )object( you can see the distribution of food groups with value_countsin [ ]pd value_counts(info group)[: out[ ]vegetables and vegetable products beef products baked products breakfast cereals legumes and legume products fast foods lambvealand game products sweets pork products fruits and fruit juices nowto do some analysis on all of the nutrient datait' easiest to assemble the nutrients for each food into single large table to do sowe need to take several steps firsti'll convert each list of food nutrients to dataframeadd column for the food idand append the dataframe to list thenthese can be concatenated together with concatnutrients [for rec in dbfnuts dataframe(rec['nutrients']fnuts['id'rec['id'nutrients append(fnutsnutrients pd concat(nutrientsignore_index=true data wranglingcleantransformmergereshape www it-ebooks info