BW #22: Banana index

Last week, we looked at the number of electric cars that are being sold (and bought) in various countries. One of the reasons why consumers and companies alike are interested in electric cars is the huge impact that internal combustion engines have on the climate. Switching to electric cars would make a big difference in the amount of carbon dioxide put into the atmosphere, and would thus help us to slow climate change.

However, other parts of our lives also affect the climate. In particular, the food that we eat can have a huge carbon footprint, or a small one. Certainly, vegetarians and vegans have been pointing to the fact that meat adds lots of carbon to the environment when compared with a plant-based diet.

But just how much worse for the environment is it to eat beef, chicken, or fish? And how exactly do we measure which is "better" and "worse"?

Leave it to the clever folks at the Economist to suggest something. In a recent issue (https://www.economist.com/graphic-detail/2023/04/11/a-different-way-to-measure-the-climate-impact-of-food), they announced their all-new "banana index," comparing the emissions generated by a single banana to number of other foods. You could thus say that a particular product creates 20 times, 50 times, or even half as much carbon as does a banana.

Moreover, the Economist's staff decided that you cannot really compare a single banana with a single serving of other foods. They thus decided to compare the foods using four different metrics:

• Emissions per kg of food
• Emissions per 1,000 kilocalories
• Emissions per 100 mg of protein
• Emissions per 100 mg of fat

(The article didn't include this final measure, per 100 mg of fat, in its final description or plot.)

So if you want to find out which has more emissions, 1 kg of apples or 1 kg of bananas, you can find out. (You'll get some pushback for doing that though, because it won't be an apples-to-apples comparison.)

And if you are hungry, and need to eat a 1,000-calorie meal, you can compare the emissions you would create from eating 1,000 calories worth of bananas (not medical advice!) or 1,000 calories worth of steak, cheddar cheese, or lettuce.

Finally, if you're one of the many people who are looking to up their protein intake, then you can compare the emissions from eating 100 mg of protein from bananas or 100 mg of protein from steak, cheddar cheese, or lettuce. (Again, not medical advice!)

I'll add that while carbon emissions are certainly important, the amount of land needed for each of these measures (kg, calories, 100 protein, and 100 mg fat) is also an important consideration. The data includes comparisons along these lines, too.

Data

This week, we'll look at the banana index from a variety of perspectives. The Economist is not only a great magazine -- I've been a subscriber for years -- but they have several active repositories on GitHub for data-related items. The repo for the banana index is at:

https://github.com/TheEconomist/banana-index-data/

We're going to download the data from version 1.0 of the banana index, in this CSV file:

https://github.com/TheEconomist/banana-index-data/releases/download/1.0/bananaindex.csv

By the way, if you're interested in bananas beyond this week's question, then I can recommend this short set of little-known facts about bananas:

https://www.pbs.org/newshour/nation/8-things-you-didnt-know-about-bananas

It has been a while, but I once read a great book about bananas ("Banana: The Fate of the Fruit That Changed the World," by Dan Koeppel), and heard an interview with the author on NPR's Fresh Air: https://freshairarchive.org/segments/bananas-uncertain-future-favorite-fruit .

Questions

This week’s learning goals include working with CSV files, filtering columns and rows, broadcasting, correlations, sorting, index operations, and using the styling features in Pandas.

I have seven questions for you to answer:

• Download the data into a data frame. Set the index to be the `entity` column. Remove the `year`, `Banana values`, `type`, and `Chart?` columns.
• Three of the columns contain pre-computed banana scores for kg, calories, and protein. For each of these columns, show the 10 highest-scoring food products.