In this article, we will analyze a dataset of cereal nutrition information using SQL. The focus lies in identifying cereals with high protein content, a crucial factor for a healthy and satisfying breakfast.
This dataset contains nutritional information for various Cereal brands. Source
Run the SQL commands here to populate the data.
The CSV dataset was converted to SQL using the CSV to JSON converter.
select id, name, calories, protein, round(protein/calories, 2) as protein_cal_ratio from cereal
order by protein_cal_ratio desc limit 5;
All-Bran with Extra Fiber, 4 grams of protein for just 50 cals.
id | name | calories | protein | protein_cal_ratio |
---|---|---|---|---|
4 | All-Bran with Extra Fiber | 50 | 4 | 0.08 |
3 | All-Bran | 70 | 4 | 0.06 |
1 | 100% Bran | 70 | 4 | 0.06 |
12 | Cheerios | 110 | 6 | 0.05 |
58 | Quaker Oatmeal | 100 | 5 | 0.05 |
SQL Output